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| basket analysis in retail: Database Marketing Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin, 2010-02-26 Database marketing is at the crossroads of technology, business strategy, and customer relationship management. Enabled by sophisticated information and communication systems, today’s organizations have the capacity to analyze customer data to inform and enhance every facet of the enterprise—from branding and promotion campaigns to supply chain management to employee training to new product development. Based on decades of collective research, teaching, and application in the field, the authors present the most comprehensive treatment to date of database marketing, integrating theory and practice. Presenting rigorous models, methodologies, and techniques (including data collection, field testing, and predictive modeling), and illustrating them through dozens of examples, the authors cover the full spectrum of principles and topics related to database marketing. This is an excellent in-depth overview of both well-known and very recent topics in customer management models. It is an absolute must for marketers who want to enrich their knowledge on customer analytics. (Peter C. Verhoef, Professor of Marketing, Faculty of Economics and Business, University of Groningen) A marvelous combination of relevance and sophisticated yet understandable analytical material. It should be a standard reference in the area for many years. (Don Lehmann, George E. Warren Professor of Business, Columbia Business School) The title tells a lot about the book's approach—though the cover reads, database, the content is mostly about customers and that's where the real-world action is. Most enjoyable is the comprehensive story – in case after case – which clearly explains what the analysis and concepts really mean. This is an essential read for those interested in database marketing, customer relationship management and customer optimization. (Richard Hochhauser, President and CEO, Harte-Hanks, Inc.) In this tour de force of careful scholarship, the authors canvass the ever expanding literature on database marketing. This book will become an invaluable reference or text for anyone practicing, researching, teaching or studying the subject. (Edward C. Malthouse, Theodore R. and Annie Laurie Sills Associate Professor of Integrated Marketing Communications, Northwestern University) |
| basket analysis in retail: Tools and Techniques for Implementing International E-Trading Tactics for Competitive Advantage Meral, Yurdagül, 2019-09-20 The use of ICT applications has dipped into almost every aspect of the business sector, including trade. With the volume of e-commerce increasing, international traders must switch their rules and practices to e-trade to survive in such a competitive market. However, the complexity of international trade, which covers customs processes, different legislation, specific documentation requirements, different languages, different currencies, and different payment systems and risk, presents its own challenges in this transition. Tools and Techniques for Implementing International E-Trading Tactics for Competitive Advantage examines the multidisciplinary approach of international e-trade as it applies to information technology, digital marketing, digital communication, online reputation management, and different legislation and risks. The content within this publication examines digital advertising, consumer behavior, and e-commerce and is designed for international traders, entrepreneurs, business professionals, researchers, academicians, and students. |
| basket analysis in retail: Retail Analytics Emmett Cox, 2011-10-18 The inside scoop on boosting sales through spot-on analytics Retailers collect a huge amount of data, but don't know what to do with it. Retail Analytics not only provides a broad understanding of retail, but also shows how to put accumulated data to optimal use. Each chapter covers a different focus of the retail environment, from retail basics and organization structures to common retail database designs. Packed with case studies and examples, this book insightfully reveals how you can begin using your business data as a strategic advantage. Helps retailers and analysts to use analytics to sell more merchandise Provides fact-based analytic strategies that can be replicated with the same success the author achieved on a global level Reveals how retailers can begin using their data as a strategic advantage Includes examples from many retail departments illustrating successful use of data and analytics Analytics is the wave of the future. Put your data to strategic use with the proven guidance found in Retail Analytics. |
| basket analysis in retail: Data Algorithms Mahmoud Parsian, 2015-07-13 If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis) |
| basket analysis in retail: R in a Nutshell Joseph Adler, 2012-10-09 Presents a guide to the R computer language, covering such topics as the user interface, packages, syntax, objects, functions, object-oriented programming, data sets, lattice graphics, regression models, and bioconductor. |
| basket analysis in retail: From Data and Information Analysis to Knowledge Engineering Myra Spiliopoulou, Rudolf Kruse, Christian Borgelt, Andreas Nürnberger, Wolfgang A. Gaul, 2006-04-20 This volume collects revised versions of papers presented at the 29th Annual Conference of the Gesellschaft für Klassifikation, the German Classification Society, held at the Otto-von-Guericke-University of Magdeburg, Germany, in March 2005. In addition to traditional subjects like Classification, Clustering, and Data Analysis, converage extends to a wide range of topics relating to Computer Science: Text Mining, Web Mining, Fuzzy Data Analysis, IT Security, Adaptivity and Personalization, and Visualization. |
| basket analysis in retail: Data Mining Techniques Michael J. A. Berry, Gordon S. Linoff, 2004-04-09 Many companies have invested in building large databases and data warehouses capable of storing vast amounts of information. This book offers business, sales and marketing managers a practical guide to accessing such information. |
| basket analysis in retail: DAX Patterns Marco Russo, Alberto Ferrari, 2020-08-10 A pattern is a general, reusable solution to a frequent or common challenge. This book is the second edition of the most comprehensive collection of ready-to-use solutions in DAX, that you can use in Microsoft Power BI, Analysis Services Tabular, and Power Pivot for Excel. The book includes the following patterns: Time-related calculations, Standard time-related calculations, Month-related calculations, Week-related calculations, Custom time-related calculations, Comparing different time periods, Semi-additive calculations, Cumulative total, Parameter table, Static segmentation, Dynamic segmentation, ABC classification, New and returning customers, Related distinct count, Events in progress, Ranking, Hierarchies, Parent-child hierarchies, Like-for-like comparison, Transition matrix, Survey, Basket analysis, Currency conversion, Budget. |
| basket analysis in retail: We Are Market Basket Daniel Korschun, Grant Welker, 2015-08-12 What if a company were so treasured and trusted that people literally took to the streets—by the thousands—to save it? That company is Market Basket, a popular New England supermarket chain. With its arresting firsthand accounts from the streets and executive suites, We Are Market Basket is as inspiring as it is instructive. What is it about Market Basket and its leader that provokes such ferocious loyalty? How does a company spread across three states maintain a culture that embraces everyone—from cashier to customer—as family? Can a company really become an industry leader by prioritizing stakeholders over shareholders? After long-time CEO Arthur T. Demoulas was ousted by his cousin Arthur S. Demoulas, the company's managers and rank-and-file workers struck back. Risking their own livelihoods to restore the job of their beloved boss they walked out, but they didn't walk far. The national media and experts were stunned by the unprecedented defense of an executive. All openly challenged the Market Basket board of directors to make things right. In the end: They were joined by loyal customers at protest rallies—leaving stores empty. Suppliers and vendors stopped deliveries—rendering shelves bare. Politicians were forced to take sides. Set against a backdrop of bad blood and corporate greed, We Are Market Basket is a page-turner that chronicles the epic rise, fall, and redemption of this iconic and uniquely American company. Note: There are links to media content within the text of this EBook which may not work on all reading devices. |
| basket analysis in retail: Descriptive Data Mining David L. Olson, Georg Lauhoff, 2019-05-06 This book provides an overview of data mining methods demonstrated by software. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Diagnostic analytics can apply analysis to sensor input to direct control systems automatically. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on descriptive analytics. The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic software support to data visualization. Chapter 3 covers fundamentals of market basket analysis, and Chapter 4 provides demonstration of RFM modeling, a basic marketing data mining tool. Chapter 5 demonstrates association rule mining. Chapter 6 is a more in-depth coverage of cluster analysis. Chapter 7 discusses link analysis. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links. |
| basket analysis in retail: Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013) Tutut Herawan, Mustafa Mat Deris, Jemal Abawajy, 2013-12-14 The proceeding is a collection of research papers presented at the International Conference on Data Engineering 2013 (DaEng-2013), a conference dedicated to address the challenges in the areas of database, information retrieval, data mining and knowledge management, thereby presenting a consolidated view to the interested researchers in the aforesaid fields. The goal of this conference was to bring together researchers and practitioners from academia and industry to focus on advanced on data engineering concepts and establishing new collaborations in these areas. The topics of interest are as follows but are not limited to: • Database theory • Data management • Data mining and warehousing • Data privacy & security • Information retrieval, integration and visualization • Information system • Knowledge discovery in databases • Mobile, grid and cloud computing • Knowledge-based • Knowledge management • Web data, services and intelligence |
| basket analysis in retail: Performance Management in Retail and the Consumer Goods Industry Michael Buttkus, Ralf Eberenz, 2019-06-21 This book offers essential insights into various management concepts for retail and consumer packaged goods companies. Addressing a range of topics in the field of performance management, it presents concepts for management control, management reporting, planning & forecasting, as well as digitization-related aspects. The contributing authors share valuable lessons learned from real-world consulting projects and present innovative approaches to successful and effective management control at retail and consumer packaged goods companies. |
| basket analysis in retail: Business Intelligence and Data Mining Anil Maheshwari, 2014-12-31 “This book is a splendid and valuable addition to this subject. The whole book is well written and I have no hesitation to recommend that this can be adapted as a textbook for graduate courses in Business Intelligence and Data Mining.” Dr. Edi Shivaji, Des Moines, Iowa “As a complete novice to this area just starting out on a MBA course I found the book incredibly useful and very easy to follow and understand. The concepts are clearly explained and make it an easy task to gain an understanding of the subject matter.” -- Mr. Craig Domoney, South Africa. Business Intelligence and Data Mining is a conversational and informative book in the exploding area of Business Analytics. Using this book, one can easily gain the intuition about the area, along with a solid toolset of major data mining techniques and platforms. This book can thus be gainfully used as a textbook for a college course. It is also short and accessible enough for a busy executive to become a quasi-expert in this area in a couple of hours. Every chapter begins with a case-let from the real world, and ends with a case study that runs across the chapters. |
| basket analysis in retail: Frequent Pattern Mining Charu C. Aggarwal, Jiawei Han, 2014-08-29 This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference. |
| basket analysis in retail: Handbook of Statistical Analysis and Data Mining Applications Ken Yale, Robert Nisbet, Gary D. Miner, 2017-11-09 Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications |
| basket analysis in retail: Applied Advanced Analytics Arnab Kumar Laha, 2021-06-08 This book covers several new areas in the growing field of analytics with some innovative applications in different business contexts, and consists of selected presentations at the 6th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. The book is conceptually divided in seven parts. The first part gives expository briefs on some topics of current academic and practitioner interests, such as data streams, binary prediction and reliability shock models. In the second part, the contributions look at artificial intelligence applications with chapters related to explainable AI, personalized search and recommendation, and customer retention management. The third part deals with credit risk analytics, with chapters on optimization of credit limits and mitigation of agricultural lending risks. In its fourth part, the book explores analytics and data mining in the retail context. In the fifth part, the book presents some applications of analytics to operations management. This part has chapters related to improvement of furnace operations, forecasting food indices and analytics for improving student learning outcomes. The sixth part has contributions related to adaptive designs in clinical trials, stochastic comparisons of systems with heterogeneous components and stacking of models. The seventh and final part contains chapters related to finance and economics topics, such as role of infrastructure and taxation on economic growth of countries and connectedness of markets with heterogenous agents, The different themes ensure that the book would be of great value to practitioners, post-graduate students, research scholars and faculty teaching advanced business analytics courses. |
| basket analysis in retail: Data Mining for Co-location Patterns Guoqing Zhou, 2022-01-26 Co-location pattern mining detects sets of features frequently located in close proximity to each other. This book focuses on data mining for co-location pattern, a valid method for identifying patterns from all types of data and applying them in business intelligence and analytics. It explains the fundamentals of co-location pattern mining, co-location decision tree, and maximal instance co-location pattern mining along with an in-depth overview of data mining, machine learning, and statistics. This arrangement of chapters helps readers understand the methods of co-location pattern mining step-by-step and their applications in pavement management, image classification, geospatial buffer analysis, etc. |
| basket analysis in retail: Computational Logic — CL 2000 John Lloyd, Veronica Dahl, Ulrich Furbach, Manfred Kerber, Kung-Kiu Lau, Catuscia Palamidessi, Luis M. Pereira, Yehoshua Sagiv, Peter J. Stuckey, 2003-06-26 These are the proceedings of the First International Conference on Compu- tional Logic (CL 2000) which was held at Imperial College in London from 24th to 28th July, 2000. The theme of the conference covered all aspects of the theory, implementation, and application of computational logic, where computational logic is to be understood broadly as the use of logic in computer science. The conference was collocated with the following events: { 6th International Conference on Rules and Objects in Databases (DOOD 2000) { 10th International Workshop on Logic-based Program Synthesis and Tra- formation (LOPSTR 2000) { 10th International Conference on Inductive Logic Programming (ILP 2000). CL 2000 consisted of seven streams: { Program Development (LOPSTR 2000) { Logic Programming: Theory and Extensions { Constraints { Automated Deduction: Putting Theory into Practice { Knowledge Representation and Non-monotonic Reasoning { Database Systems (DOOD 2000) { Logic Programming: Implementations and Applications. The LOPSTR 2000 workshop constituted the program development stream and the DOOD 2000 conference constituted the database systems stream. Each stream had its own chair and program committee, which autonomously selected the papers in the area of the stream. Overall, 176 papers were submitted, of which 86 were selected to be presented at the conference and appear in these proceedings. The acceptance rate was uniform across the streams. In addition, LOPSTR 2000 accepted about 15 extended abstracts to be presented at the conference in the program development stream. |
| basket analysis in retail: R for Marketing Research and Analytics Chris Chapman, Elea McDonnell Feit, 2015-03-25 This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications. |
| basket analysis in retail: Using Information to Develop a Culture of Customer Centricity David Loshin, Abie Reifer, 2013-11-22 Using Information to Develop a Culture of Customer Centricity sets the stage for understanding the holistic marriage of information, socialization, and process change necessary for transitioning an organization to customer centricity. The book begins with an overview list of 8-10 precepts associated with a business-focused view of the knowledge necessary for developing customer-oriented business processes that lead to excellent customer experiences resulting in increased revenues. Each chapter delves into each precept in more detail. |
| basket analysis in retail: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| basket analysis in retail: Innovations in Computer Science and Engineering H. S. Saini, Rishi Sayal, Aliseri Govardhan, Rajkumar Buyya, 2019-06-18 This book includes high-quality, peer-reviewed research papers from the 6thInternational Conference on Innovations in Computer Science & Engineering (ICICSE 2018), held at Guru Nanak Institutions, Hyderabad, India from August 17 to 18, 2018. The book discusses a wide variety of industrial, engineering and scientific applications of the emerging techniques and offers a platform for researchers from academia and industry to present their original work and exchange ideas, information, techniques and applications in the field of computer science. |
| basket analysis in retail: Principles and Applications of Business Intelligence Research Herschel, Richard T., 2012-12-31 This book provides the latest ideas and research on advancing the understanding and implementation of business intelligence within organizations--Provided by publisher. |
| basket analysis in retail: Data Science with SQL Server Quick Start Guide Dejan Sarka, 2018-08-31 Get unique insights from your data by combining the power of SQL Server, R and Python Key Features Use the features of SQL Server 2017 to implement the data science project life cycle Leverage the power of R and Python to design and develop efficient data models find unique insights from your data with powerful techniques for data preprocessing and analysis Book Description SQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you. This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. It covers all stages of a data science project, from businessand data understanding,through data overview, data preparation, modeling and using algorithms, model evaluation, and deployment. You will learn to use the engines and languages that come with SQL Server, including ML Services with R and Python languages and Transact-SQL. You will also learn how to choose which algorithm to use for which task, and learn the working of each algorithm. What you will learn Use the popular programming languages,T-SQL, R, and Python, for data science Understand your data with queries and introductory statistics Create and enhance the datasets for ML Visualize and analyze data using basic and advanced graphs Explore ML using unsupervised and supervised models Deploy models in SQL Server and perform predictions Who this book is for SQL Server professionals who want to start with data science, and data scientists who would like to start using SQL Server in their projects will find this book to be useful. Prior exposure to SQL Server will be helpful. |
| basket analysis in retail: Applied Data Mining for Business and Industry Paolo Giudici, Silvia Figini, 2009-04-15 The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications. Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods. Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining. Features detailed case studies based on applied projects within industry. Incorporates discussion of data mining software, with case studies analysed using R. Is accessible to anyone with a basic knowledge of statistics or data analysis. Includes an extensive bibliography and pointers to further reading within the text. Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will provide guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance. |
| basket analysis in retail: Storytelling with Data Cole Nussbaumer Knaflic, 2015-10-09 Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it! |
| basket analysis in retail: Data Analysis, Machine Learning and Applications Christine Preisach, Hans Burkhardt, Lars Schmidt-Thieme, Reinhold Decker, 2008-04-13 Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007. |
| basket analysis in retail: Drawdown Paul Hawken, 2017-04-18 • New York Times bestseller • The 100 most substantive solutions to reverse global warming, based on meticulous research by leading scientists and policymakers around the world “At this point in time, the Drawdown book is exactly what is needed; a credible, conservative solution-by-solution narrative that we can do it. Reading it is an effective inoculation against the widespread perception of doom that humanity cannot and will not solve the climate crisis. Reported by-effects include increased determination and a sense of grounded hope.” —Per Espen Stoknes, Author, What We Think About When We Try Not To Think About Global Warming “There’s been no real way for ordinary people to get an understanding of what they can do and what impact it can have. There remains no single, comprehensive, reliable compendium of carbon-reduction solutions across sectors. At least until now. . . . The public is hungry for this kind of practical wisdom.” —David Roberts, Vox “This is the ideal environmental sciences textbook—only it is too interesting and inspiring to be called a textbook.” —Peter Kareiva, Director of the Institute of the Environment and Sustainability, UCLA In the face of widespread fear and apathy, an international coalition of researchers, professionals, and scientists have come together to offer a set of realistic and bold solutions to climate change. One hundred techniques and practices are described here—some are well known; some you may have never heard of. They range from clean energy to educating girls in lower-income countries to land use practices that pull carbon out of the air. The solutions exist, are economically viable, and communities throughout the world are currently enacting them with skill and determination. If deployed collectively on a global scale over the next thirty years, they represent a credible path forward, not just to slow the earth’s warming but to reach drawdown, that point in time when greenhouse gases in the atmosphere peak and begin to decline. These measures promise cascading benefits to human health, security, prosperity, and well-being—giving us every reason to see this planetary crisis as an opportunity to create a just and livable world. |
| basket analysis in retail: Artificial Intelligence Applications and Innovations Lazaros S. Iliadis, Ilias Maglogiannis, Harris Papadopoulos, 2013-11-27 The two-volume set IFIP AICT 363 and 364 constitutes the refereed proceedings of the 12th International Conference on Engineering Applications of Neural Networks, EANN 2011, and the 7th IFIP WG 12.5 International Conference, AIAI 2011, held jointly in Corfu, Greece, in September 2011. The 52 revised full papers and 28 revised short papers presented together with 31 workshop papers were carefully reviewed and selected from 150 submissions. The second volume includes the papers that were accepted for presentation at the AIAI 2011 conference. They are organized in topical sections on computer vision and robotics, classification/pattern recognition, financial and management applications of AI, fuzzy systems, learning and novel algorithms, recurrent and radial basis function ANN, machine learning, generic algorithms, data mining, reinforcement learning, Web applications of ANN, medical applications of ANN and ethics of AI, and environmental and earth applications of AI. The volume also contains the accepted papers from the First Workshop on Computational Intelligence in Software Engineering (CISE 2011) and the Workshop on Artificial Intelligence Applications in Biomedicine (AIAB 2011). |
| basket analysis in retail: Proceedings of International Conference on Communication and Computational Technologies Sunil Dutt Purohit, Dharm Singh Jat, Ramesh Chandra Poonia, Sandeep Kumar, Saroj Hiranwal, 2020-08-27 This book offers a collection of high-quality peer-reviewed research papers presented at the Second International Conference on Communication and Computational Technologies (ICCCT 2019), held at Rajasthan Institute of Engineering and Technology, Jaipur, Rajasthan, India, on 30–31 August 2019. In contributions prepared by researchers from academia and industry alike, the book discusses a wide variety of industrial, engineering and scientific applications of emerging techniques. |
| basket analysis in retail: Machine Learning in Java Bostjan Kaluza, 2016-04-29 Design, build, and deploy your own machine learning applications by leveraging key Java machine learning librariesAbout This Book- Develop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries- Explore a broad variety of data processing, machine learning, and natural language processing through diagrams, source code, and real-world applications- Packed with practical advice and tips to help you get to grips with applied machine learningWho This Book Is ForIf you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life. You should be familiar with Java programming and data mining concepts to make the most of this book, but no prior experience with data mining packages is necessary.What You Will Learn- Understand the basic steps of applied machine learning and how to differentiate among various machine learning approaches- Discover key Java machine learning libraries, what each library brings to the table, and what kind of problems each are able to solve- Learn how to implement classification, regression, and clustering- Develop a sustainable strategy for customer retention by predicting likely churn candidates- Build a scalable recommendation engine with Apache Mahout- Apply machine learning to fraud, anomaly, and outlier detection- Experiment with deep learning concepts, algorithms, and the toolbox for deep learning- Write your own activity recognition model for eHealth applications using mobile sensorsIn DetailAs the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering.Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will explore related web resources and technologies that will help you take your learning to the next level.By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.Style and approachThis is a practical tutorial that uses hands-on examples to step through some real-world applications of machine learning. Without shying away from the technical details, you will explore machine learning with Java libraries using clear and practical examples. You will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process. |
| basket analysis in retail: Predictive Analytics Using Oracle Data Miner Brendan Tierney, 2014-08-08 Build Next-Generation In-Database Predictive Analytics Applications with Oracle Data Miner “If you have an Oracle Database and want to leverage that data to discover new insights, make predictions, and generate actionable insights, this book is a must read for you! In Predictive Analytics Using Oracle Data Miner: Develop & Use Oracle Data Mining Models in Oracle Data Miner, SQL & PL/SQL, Brendan Tierney, Oracle ACE Director and data mining expert, guides you through the basic concepts of data mining and offers step-by-step instructions for solving data-driven problems using SQL Developer’s Oracle Data Mining extension. Brendan takes it full circle by showing you how to deploy advanced analytical methodologies and predictive models immediately into enterprise-wide production environments using the in-database SQL and PL/SQL functionality. Definitely a must read for any Oracle data professional!” --Charlie Berger, Senior Director Product Management, Oracle Data Mining and Advanced Analytics Perform in-database data mining to unlock hidden insights in data. Written by an Oracle ACE Director, Predictive Analytics Using Oracle Data Miner shows you how to use this powerful tool to create and deploy advanced data mining models. Covering topics for the data scientist, Oracle developer, and Oracle database administrator, this Oracle Press guide shows you how to get started with Oracle Data Miner and build Oracle Data Miner models using SQL and PL/SQL packages. You'll get best practices for integrating your Oracle Data Miner models into applications to automate the discovery and distribution of business intelligence predictions throughout the enterprise. Install and configure Oracle Data Miner for Oracle Database 11g Release 11.2 and Oracle Database 12c Create Oracle Data Miner projects and workflows Prepare data for data mining Develop data mining models using association rule analysis, classification, clustering, regression, and anomaly detection Use data dictionary views and prepare your data using in-database transformations Build and use data mining models using SQL and PL/SQL packages Migrate your Oracle Data Miner models, integrate them into dashboards and applications, and run them in parallel Build transient data mining models with the Predictive Queries feature in Oracle Database 12c |
| basket analysis in retail: Pro Tableau Seema Acharya, Subhashini Chellappan, 2016-12-23 Leverage the power of visualization in business intelligence and data science to make quicker and better decisions. Use statistics and data mining to make compelling and interactive dashboards. This book will help those familiar with Tableau software chart their journey to being a visualization expert. Pro Tableau demonstrates the power of visual analytics and teaches you how to: Connect to various data sources such as spreadsheets, text files, relational databases (Microsoft SQL Server, MySQL, etc.), non-relational databases (NoSQL such as MongoDB, Cassandra), R data files, etc. Write your own custom SQL, etc. Perform statistical analysis in Tableau using R Use a multitude of charts (pie, bar, stacked bar, line, scatter plots, dual axis, histograms, heat maps, tree maps, highlight tables, box and whisker, etc.) What you’ll learn Connect to various data sources such as relational databases (Microsoft SQL Server, MySQL), non-relational databases (NoSQL such as MongoDB, Cassandra), write your own custom SQL, join and blend data sources, etc. Leverage table calculations (moving average, year over year growth, LOD (Level of Detail), etc. Integrate Tableau with R Tell a compelling story with data by creating highly interactive dashboards Who this book is for All levels of IT professionals, from executives responsible for determining IT strategies to systems administrators, to data analysts, to decision makers responsible for driving strategic initiatives, etc. The book will help those familiar with Tableau software chart their journey to a visualization expert. |
| basket analysis in retail: The Retail Revolution Nelson Lichtenstein, 2009-07-21 The definitive account of how a small Ozarks company upended the world of business and what that change means Wal-Mart, the world's largest company, roared out of the rural South to change the way business is done. Deploying computer-age technology, Reagan-era politics, and Protestant evangelism, Sam Walton's firm became a byword for cheap goods and low-paid workers, famed for the ruthless efficiency of its global network of stores and factories. But the revolution has gone further: Sam's protégés have created a new economic order which puts thousands of manufacturers, indeed whole regions, in thrall to a retail royalty. Like the Pennsylvania Railroad and General Motors in their heyday, Wal-Mart sets the commercial model for a huge swath of the global economy. In this lively, probing investigation, historian Nelson Lichtenstein deepens and expands our knowledge of the merchandising giant. He shows that Wal-Mart's rise was closely linked to the cultural and religious values of Bible Belt America as well as to the imperial politics, deregulatory economics, and laissez-faire globalization of Ronald Reagan and his heirs. He explains how the company's success has transformed American politics, and he anticipates a day of reckoning, when challenges to the Wal-Mart way, at home and abroad, are likely to change the far-flung empire. Insightful, original, and steeped in the culture of retail life, The Retail Revolution draws on first hand reporting from coastal China to rural Arkansas to give a fresh and necessary understanding of the phenomenon that has transformed international commerce. |
| basket analysis in retail: Advanced Techniques in Knowledge Discovery and Data Mining Nikhil Pal, 2005-07-01 Clear and concise explanations to understand the learning paradigms. Chapters written by leading world experts. |
| basket analysis in retail: Retail Buying Richard Clodfelter, 2015-03-05 This comprehensive book provides students with the skills and savvy needed to become successful buyers in any area of retail. With a simple and straightforward approach, Clodfelter presents step-by-step instructions for typical buying tasks, such as identifying and understanding potential customers, creating a six-month merchandising plan, and developing sales forecasts. With coverage of math concepts integrated throughout the text, this new edition contains up-to-date coverage of important retailing trends, including more coverage of international buying and sourcing, integration of product development concepts throughout, and more math practice problems in chapters. Updated Snapshot and Trendwatch features present current info and new case studies from the fashion industry.Ample activities-drawn from real-world merchandising and incorporating current trends-give students the opportunity to apply critical skills as they would in a professional environment. New to This Edition: ~STUDIO: Retail Buying Studio features online self-quizzes, flashcards, math practic problems and Excel spreadsheet activities that align with chapter Spreadsheet Skills activities ~Additional math practice problems in end of chapter activities ~More than 20% new photographs throughout the book ~30% new Snapshot and Trendwatch features and updated content in all cases ~Expanded coverage of buying in foreign markets ~Integrated content on product development throughout PLEASE NOTE: Purchasing or renting this ISBN does not include access to the STUDIO resources that accompany this text. To receive free access to the STUDIO content with new copies of this book, please refer to the book + STUDIO access card bundle ISBN 9781501395260. STUDIO Instant Access can also be purchased or rented separately on BloomsburyFashionCentral.com. |
| basket analysis in retail: Big Data Analytics Kiran Chaudhary, Mansaf Alam, 2022-01-19 Big Data Analytics: Applications in Business and Marketing explores the concepts and applications related to marketing and business as well as future research directions. It also examines how this emerging field could be extended to performance management and decision-making. Investment in business and marketing analytics can create value through proper allocation of resources and resource orchestration process. The use of data analytics tools can be used to diagnose and improve performance. The book is divided into five parts. The first part introduces data science, big data, and data analytics. The second part focuses on applications of business analytics including: Big data analytics and algorithm Market basket analysis Anticipating consumer purchase behavior Variation in shopping patterns Big data analytics for market intelligence The third part looks at business intelligence and features an evaluation study of churn prediction models for business Intelligence. The fourth part of the book examines analytics for marketing decision-making and the roles of big data analytics for market intelligence and of consumer behavior. The book concludes with digital marketing, marketing by consumer analytics, web analytics for digital marketing, and smart retailing. This book covers the concepts, applications and research trends of marketing and business analytics with the aim of helping organizations increase profitability by improving decision-making through data analytics. |
| basket analysis in retail: Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018 Leszek Borzemski, Jerzy Świątek, Zofia Wilimowska, 2018-08-28 This three-volume set of books highlights major advances in the development of concepts and techniques in the area of new technologies and architectures of contemporary information systems. Further, it helps readers solve specific research and analytical problems and glean useful knowledge and business value from the data. Each chapter provides an analysis of a specific technical problem, followed by a numerical analysis, simulation and implementation of the solution to the real-life problem. Managing an organisation, especially in today’s rapidly changing circumstances, is a very complex process. Increased competition in the marketplace, especially as a result of the massive and successful entry of foreign businesses into domestic markets, changes in consumer behaviour, and broader access to new technologies and information, calls for organisational restructuring and the introduction and modification of management methods using the latest advances in science. This situation has prompted many decision-making bodies to introduce computer modelling of organisation management systems. The three books present the peer-reviewed proceedings of the 39th International Conference “Information Systems Architecture and Technology” (ISAT), held on September 16–18, 2018 in Nysa, Poland. The conference was organised by the Computer Science and Management Systems Departments, Faculty of Computer Science and Management, Wroclaw University of Technology and Sciences and University of Applied Sciences in Nysa, Poland. The papers have been grouped into three major parts: Part I—discusses topics including but not limited to Artificial Intelligence Methods, Knowledge Discovery and Data Mining, Big Data, Knowledge Based Management, Internet of Things, Cloud Computing and High Performance Computing, Distributed Computer Systems, Content Delivery Networks, and Service Oriented Computing. Part II—addresses topics including but not limited to System Modelling for Control, Recognition and Decision Support, Mathematical Modelling in Computer System Design, Service Oriented Systems and Cloud Computing, and Complex Process Modelling. Part III—focuses on topics including but not limited to Knowledge Based Management, Modelling of Financial and Investment Decisions, Modelling of Managerial Decisions, Production Systems Management and Maintenance, Risk Management, Small Business Management, and Theories and Models of Innovation. |
| basket analysis in retail: Oracle 10g Data Warehousing Lilian Hobbs, Susan Hillson, Shilpa Lawande, Pete Smith, 2011-04-18 Oracle 10g Data Warehousing is a guide to using the Data Warehouse features in the latest version of Oracle —Oracle Database 10g. Written by people on the Oracle development team that designed and implemented the code and by people with industry experience implementing warehouses using Oracle technology, this thoroughly updated and extended edition provides an insider's view of how the Oracle Database 10g software is best used for your application.It provides a detailed look at the new features of Oracle Database 10g and other Oracle products and how these are used in the data warehouse. This book will show you how to deploy the Oracle database and correctly use the new Oracle Database 10g features for your data warehouse. It contains walkthroughs and examples on how to use tools such as Oracle Discoverer and Reports to query the warehouse and generate reports that can be deployed over the web and gain better insight into your business.This how-to guide provides step by step instructions including screen captures to make it easier to design, build and optimize performance of the data warehouse or data mart. It is a 'must have' reference for database developers, administrators and IT professionals who want to get to work now with all of the newest features of Oracle Database 10g.It provides a detailed look at the new features of Oracle Database 10g and other Oracle products and how these are used in the data warehouse - How to use the Summary Management features, including Materialized Views and query rewrite, to best effect to radically improve query performance - How to deploy business intelligence to the Web to satisfy today's changing and demanding business requirements - Using Oracle OLAP and Data Mining options - How to understand the warehouse hardware environment and how it is used by new features in the database including how to implement a high availability warehouse environment - Using the new management infrastructure in Oracle Database 10g and how this helps you to manage your warehouse environment |
| basket analysis in retail: The Stray Shopping Carts of Eastern North America Julian Montague, 2023-10-30 A taxonomy we didn’t know we needed for identifying and cataloging stray shopping carts by artist and photographer Julian Montague. Abandoned shopping carts are everywhere, and yet we know so little about them. Where do they come from? Why are they there? Their complexity and history baffle even the most careful urban explorer. Thankfully, artist Julian Montague has created a comprehensive and well-documented taxonomy with The Stray Shopping Carts of Eastern North America. Spanning thirty-three categories from damaged, fragment, and plow crush to plaza drift and bus stop discard, it is a tonic for times defined increasingly by rhetoric and media and less by the plain objects and facts of the real world. Montague’s incomparable documentation of this common feature of the urban landscape helps us see the natural and man-made worlds—and perhaps even ourselves—anew. First published in 2006 to great perplexity and acclaim alike, Montague’s book now appears in refreshed and expanded form. Told in an exceedingly dry voice, with full-color illustrations and photographs throughout, it is both rigorous and absurd, offering a strangely compelling vision of how we approach, classify, and understand the environments around us. A new afterword sheds light on the origins of the project. |
Market Basket Analysis in Retail - UPC Universitat Politècnica …
Market basket analysis [Kamakura, 2012] encompasses a broad set of analytics techniques aimed at uncovering the associations and connections between specific objects, discovering …
MARKET BASKET ANALYSIS: A COMPREHENSIVE SURVEY
Market Basket Analysis (MBA) has emerged as a pivotal technique for analyzing transaction data, revealing hidden patterns in customer purchases. By examining the combinations of products …
MARKET BASKET ANALYSIS FOR RETAIL OPTIMIZATION: …
Market Basket Analysis (MBA) is a pivotal data mining technique employed to discern patterns in customer purchasing behaviors by identifying associations among products frequently bought …
Market Basket Analysis using Machine Learning
Market basket analysis (MBA) is one such top retail application of machine learning. It helps retailers know what products people are purchasing together so that the store/website layout …
Using Market Basket Analysis to Increase Sales - Infocepts …
One of these tools is Market Basket Analysis. This technique helps retailers identify which items a customer is more (or less) likely to buy, given a previous purchase or a contemplated …
Leading Practices in Market Basket Analysis - FactPoint
evolution of retail merchandising and promotion. Market basket analysis allows leading retailers to quickly and easily look at the size, contents, and value of their customer’s market basket to …
Market Basket Analysis for Retail Sales Optimization
The proposed system “Market basket analysis for retail sales optimization” introduces a data-driven solution that leverages association rule mining algorithms, namely Eclat, FP-growth, …
Market Basket Analysis - UNL
Market basket analysis encompasses a broad set of analytic techniques aimed at uncovering the associations and connections between specific objects, discovering customer behaviors and …
Market Basket Analysis of Retail Data: Supervised
In this work we discuss a supervised learning approach for identifica-tion of frequent itemsets and association rules from transactional data. This task is typically encountered in market basket...
Market Basket Analysis for Sales Transaction in Shopping …
Market Basket Analysis (MBA) system is a widely used technique among marketers, especially for undirected data mining analysis. MBA is also known as product association analysis and the …
Using Big Data Analytics to Design an Intelligent Market …
intelligent market basket analysis to help in shoring up customer relationships. This study uses big data analytics to design and analyze intelligent market basket in one top retailer in Jordan, …
Designing a Data Warehouse for Market Basket Analysis in …
Jan 12, 2010 · Market basket analysis can help to identify items that are frequently bought together, and this can be used for various purposes such as store layout design and campaigns.
A STUDY ON MARKET BASKET ANALYSIS FOR A …
Market Basket Analysis is a powerful data mining technique used to identify clusters of items commonly bought together in vast datasets or databases. Widely applied in sectors such as …
Market basket analysis with association rules in the retail …
This research analyzes the shopping basket by using association rules in the retail area, speci cally in a home goods sales company such as appliances, computer items, furniture, and …
Market Basket Analysis in Retail - UPC Universitat Politècnica …
Market basket analysis [3] encompasses a broad set of analytics techniques aimed at uncovering the associations and connections between specific objects, discovering customers behaviours …
Implementation of Association Rule-Market Basket Analysis in ...
Market Basket Analysis is an analysis technique of customer habits when shopping by finding associations and correlations between various kinds of items that customers put in their …
Optimization of Store Layout using Market Basket Analysis
Market basket analysis (MBA) is a tool under marketing analytics to study the buying behaviour of customers. It is utilized to determine what items are mostly bought together or placed in the …
Retail Product Bundling: A New Approach - SAS
Affinity analysis is referred to as Market Basket Analysis in retail and e-commerce outlets application. It determines how often items are purchased together and the best possible …
Market Basket Analysis for a Supermarket based on Frequent
Market basket analysis is an important component of analytical system in retail organizations to determine the placement of goods, designing sales promotions for different segments of …
Layout Optimization and Promotional Strategies Design in a …
In this paper we present a case study for analyzing retail transactional data using MBA and use the results as a prescriptive model for sell floor optimal design and for guiding the design of …
Market Basket Analysis in Retail - UPC Universitat Poli…
Market basket analysis [Kamakura, 2012] encompasses a broad set of analytics techniques aimed at uncovering the associations and connections between …
MARKET BASKET ANALYSIS: A COMPREHENSIVE SURVEY
Market Basket Analysis (MBA) has emerged as a pivotal technique for analyzing transaction data, revealing hidden patterns in customer purchases. …
MARKET BASKET ANALYSIS FOR RETAIL OPTIMIZATIO…
Market Basket Analysis (MBA) is a pivotal data mining technique employed to discern patterns in customer purchasing behaviors by identifying associations among …
Market Basket Analysis using Machine Learning
Market basket analysis (MBA) is one such top retail application of machine learning. It helps retailers know what products people are purchasing together so that the …
Using Market Basket Analysis to Increase Sales - Infocept…
One of these tools is Market Basket Analysis. This technique helps retailers identify which items a customer is more (or less) likely to buy, given a previous …
