Descriptive Data Mining (eBook)

David L. Olson is the James & H.K. Stuart Professor in MIS and Chancellor's Professor at the University of Nebraska. He has published over 200 articles in refereed journals, primarily on the topic of multiple objective decision-making and information technology. He has authored over 20 books, is co-editor-in-chief of the International Journal of Services Sciences and associate editor of a number of journals. He has given over 150 presentations at international and national conferences. He is a member of the Decision Sciences Institute, the Institute for Operations Research and Management Sciences, and the Multiple Criteria Decision Making Society. He was a Lowry Mays endowed Professor at Texas A&M University from 1999 to 2001, was named the Raymond E. Miles Distinguished Scholar in 2002, and was James C. and Rhonda Seacrest Fellow from 2005 to 2006. He was named Best Enterprise Information Systems Educator by IFIP in 2006. He is a Fellow of the Decision Sciences Institute.

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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.