Descriptive Data Mining by David L. OlsonDescriptive Data Mining by David L. Olson

Descriptive Data Mining

byDavid L. Olson

Paperback | July 5, 2018

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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.

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 ...
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Title:Descriptive Data MiningFormat:PaperbackDimensions:116 pagesPublished:July 5, 2018Publisher:Springer NatureLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:9811098476

ISBN - 13:9789811098475

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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.