Descriptive Data Mining

✍ Scribed by David L. Olson (auth.)

• Publisher: Springer Singapore
• Year: 2017
• Tongue: English
• Leaves: 120
• Series: Computational Risk Management
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xi
Knowledge Management....Pages 1-7
Data Visualization....Pages 9-28
Market Basket Analysis....Pages 29-41
Recency Frequency and Monetary Model....Pages 43-59
Association Rules....Pages 61-69
Cluster Analysis....Pages 71-95
Link Analysis....Pages 97-111
Descriptive Data Mining....Pages 113-114
Back Matter....Pages 115-116

✦ Subjects

Big Data/Analytics;Data Mining and Knowledge Discovery;Risk Management