Descriptive Data Mining

by David L. Olson

Overview

This book offers an overview of knowledge management. It starts with an introduction to the subject, placing descriptive models in the context of the overall field as well as within the more specific field of data mining analysis. Chapter 2 covers data visualization, including directions for accessing R open source software (described through Rattle). Both R and Rattle are free to students. Chapter 3 then describes market basket analysis, comparing it with more advanced models, and addresses the concept of lift. Subsequently, Chapter 4 describes smarketing RFM models and compares it with more advanced predictive models. Next, Chapter 5 describes association rules, including the APriori algorithm and provides software support from R. Chapter 6 covers cluster analysis, including software support from R (Rattle), KNIME, and WEKA, all of which are open source. Chapter 7 goes on to describe link analysis, social network metrics, and open source NodeXL software, and demonstrates link analysis application using PolyAnalyst output. Chapter 8 concludes the monograph.
Using business-related data to demonstrate models, this descriptive book explains how methods work with some citations, but without detailed references. The data sets and software selected are widely available and can easily be accessed.

Product Details

ISBN-13:	9789811033407
Publisher:	Springer-Verlag New York, LLC
Publication date:	12/09/2016
Series:	Computational Risk Management
Sold by:	Barnes & Noble
Format:	eBook
Pages:	116
File size:	4 MB

About the Author

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.

Table of Contents

Chapter 1 Knowledge Management.- Chapter 2: Data Visualization.- Chapter 3 Market Basket Analysis.- Chapter 4 Recency Frequency and Monetary Model.- Chapter 5 Association Rules.- Chapter 6 Cluster Analysis.- Chapter 7 Link Analysis.- Chapter 7 Link Analysis.- Chapter 8 Descriptive Data Mining.- References.- Index.

Editorial Reviews

The book is direct and easy to read, explaining how methods work without in-depth scholarly references. … this is a suitable book for data mining newcomers who are not interested in a theoretical understanding of the algorithms. … the book could be used as a course resource.” (Evangelia Kavakli, Computing Reviews, November 2, 2020)

From the Publisher