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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| Main Author | |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
Singapore :
Springer Nature,
[2017]
|
| Series | Computational risk management.
|
| Subjects | |
| Online Access | Full text |
| ISBN | 9789811033407 9789811033391 |
| ISSN | 2191-1444 |
| Physical Description | 1 online resource |
Cover
Table of Contents:
- 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.
- 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.
- Link Analysis Application with PolyAnalyst (Olson and Shi 2007); Summary; References; 8 Descriptive Data Mining; Index.