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Descriptive data mining

Olson, David L.

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Descriptive data mining

Record Type:	Electronic resources : Monograph/item
Title/Author:	Descriptive data mining/ by David L. Olson.
Author:	Olson, David L.
Published:	Singapore :Springer Singapore : : 2017.,
Description:	xi, 116 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Data mining. -
Online resource:	http://dx.doi.org/10.1007/978-981-10-3340-7
ISBN:	9789811033407

Descriptive data mining
Olson, David L.

Descriptive data mining[electronic resource] /by David L. Olson. - Singapore :Springer Singapore :2017. - xi, 116 p. :ill., digital ;24 cm. - Computational risk management,2191-1436. - Computational risk management..

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.

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.

ISBN: 9789811033407

Standard No.: 10.1007/978-981-10-3340-7doiSubjects--Topical Terms:

562972
Data mining.

LC Class. No.: HD30.2 / .O47 2017

Dewey Class. No.: 006.312

Descriptive data mining
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245 1 0 $a Descriptive data mining $h [electronic resource] / $c by David L. Olson.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2017.
300 $a xi, 116 p. : $b ill., digital ; $c 24 cm.
490 1 $a Computational risk management, $x 2191-1436
505 0 $a 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.
520 $a 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.
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650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Risk Management. $3 608953
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
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