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.

Provides a comprehensive overview of knowledge management, big data, and basic descriptive data mining methods and software Illustrates concepts with typical data Demonstrates readily available open source software

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David L. Olson is the James & H.K. Stuart Professor in MIS and Chancellor's Professor at the University of Nebraska-Lincoln, USA. He has published research in over 200 refereed journal articles and has authored over 40 books. He has served as associate editor of a number of journals and made hundreds of presentations at international and national conferences on research topics. 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. He was named Best Enterprise Information Systems Educator by the IFIP in 2006. He is a Fellow of the Decision Sciences Institute. Dr. Özgür Araz is the Ron and Carol Cope Professor and Professor of Supply Chain Management and Analytics at the University of Nebraska-Lincoln, USA. His research interests include systems simulation, business analytics, healthcare operations and public health informatics. His research has been supported by the NIH, Veterans Engineering Resource Center (VERC), HDR company, Boys Town of Nebraska, Nebraska Medicine and the University of Nebraska. Before joining the College of Business at UNL, he served at the College of Public Health at the University of Nebraska Medical Center (UNMC). He received his Ph.D. in Industrial Engineering from Arizona State University and was a postdoctoral research fellow at the Center for Computational Biology and Bioinformatics of the University of Texas at Austin. He is an editorial advisory board member of the Transportation Research Part E and also serves as associate editor for Decision Sciences and IISE Transactions on Healthcare Systems Engineering. He is the Public Health Informatics Area Editor for the journal Health Systems. He is also a faculty fellow of the Nebraska Governance and Technology Center and Daugherty Water for Food Global Institute.

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