Descriptive data mining

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 accessi...

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Bibliographic Details
Main Author: Olson, David L., 1944- (Author)
Format: eBook
Language: English
Published: Singapore : Springer Nature, [2017]
Series: Computational risk management.
Subjects:
Data mining.
Big data.
Risk management.
elektronické knihy
electronic books
ISBN: 9789811033407
9789811033391
Physical Description: 1 online resource

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Table of contents

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100 1 |a Olson, David L.,  |d 1944-  |e author. 
245 1 0 |a Descriptive data mining /  |c David L. Olson. 
264 1 |a Singapore :  |b Springer Nature,  |c [2017] 
300 |a 1 online resource 
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490 1 |a Computational risk management,  |x 2191-1444 
505 0 |a Book Concept; Preface; Contents; 1 Knowledge Management; Computer Support Systems; Examples of Knowledge Management; Data Mining Descriptive Applications; Summary; References; 2 Data Visualization; Data Visualization; R Software; Loan Data; Energy Data; Basic Visualization of Time Series; Conclusion; References; 3 Market Basket Analysis; Definitions; Co-occurrence; Demonstration; Fit; Profit; Lift; Market Basket Limitations; References; 4 Recency Frequency and Monetary Model; Dataset 1; Balancing Cells; Lift; Value Function; Data Mining Classification Models; Logistic Regression. 
505 8 |a Decision Tree; Neural Networks; Dataset 2; Conclusions; References; 5 Association Rules; Methodology; The APriori Algorithm; Association Rules from Software; Non-negative Matric Factorization; Conclusion; References; 6 Cluster Analysis; K-Means Clustering; A Clustering Algorithm; Anchor 4; Clustering Methods Used in Software; Software; R (Rattle) K-Means Clustering; Other R Clustering Algorithms; KNIME; WEKA; Software Output for Original Data; R Clustering; WEKA; Summary; References; 7 Link Analysis; Link Analysis Terms; Basic Network Graphics with NodeXL. 
505 8 |a Link Analysis Application with PolyAnalyst (Olson and Shi 2007); Summary; References; 8 Descriptive Data Mining; Index. 
504 |a Includes bibliographical references at the end of each chapters and index. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
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 0 |a Big data. 
650 0 |a Risk management. 
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830 0 |a Computational risk management. 
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