| Description |
1 online resource (275 pages) |
| Series |
Chapman & Hall/CRC Computer Science & Data Analysis |
|
Chapman & Hall/CRC Computer Science & Data Analysis
|
| Contents |
Book Cover; Title; Copyright; Contents; Foreword; Foreword; Foreword; Preface; List of Tables; List of Figures; Chapter 1 Introduction; Chapter 2 Multiscale Data Condensation; Chapter 3 Unsupervised Feature Selection; Chapter 4 Active Learning Using Support Vector Machine; Chapter 5 Rough-fuzzy Case Generation; Chapter 6 Rough-fuzzy Clustering; Chapter 7 Rough Self-Organizing Map; Chapter 8 Classi.cation, Rule Generation and Evaluation using Modular Rough-fuzzy MLP; Appendix A Role of Soft-Computing Tools in KDD; Appendix B Data Sets Used in Experiments; References; Index; About the Authors |
| Summary |
Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks |
| Notes |
Print version record |
| Form |
Electronic book
|
| Author |
Mitra, Pabitra
|
| ISBN |
9780203998076 |
|
0203998073 |
|
