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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000003494824 | QA76.9.S63 S623 2008 | Open Access Book | Book | Searching... |
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Summary
Summary
Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability.
Soft Computing for Knowledge Discovery and Data Mining introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining. This edited volume by highly regarded authors, includes several contributors of the 2005, Data Mining and Knowledge Discovery Handbook. This book was written to provide investigators in the fields of information systems, engineering, computer science, statistics and management with a profound source for the role of soft computing in data mining. Not only does this book feature illustrations of various applications including manufacturing, medical, banking, insurance and others, but also includes various real-world case studies with detailed results.
Soft Computing for Knowledge Discovery and Data Mining is designed for practitioners and researchers in industry. Practitioners and researchers may be particularly interested in the description of real world data mining projects performed with soft computing. This book is also suitable as a secondary textbook or reference for advanced-level students in information systems, engineering, computer science and statistics management.
Table of Contents
| Introduction to Soft Computing for Knowledge Discovery and Data Mining | p. 1 |
| Part I Neural Network Methods | |
| Neural Networks For Data Mining | p. 17 |
| Improved SOM Labeling Methodology for Data Mining Applications | p. 45 |
| Part II Evolutionary Methods | |
| A Review of Evolutionary Algorithms for Data Mining | p. 79 |
| Genetic Clustering for Data Mining | p. 113 |
| Discovering New Rule Induction Algorithms with Grammar-based Genetic Programming | p. 133 |
| Evolutionary Design of Code-matrices for Multiclass Problems | p. 153 |
| Part III Fuzzy Logic Methods | |
| The Role of Fuzzy Sets in Data Mining | p. 187 |
| Support Vector Machines and Fuzzy Systems | p. 205 |
| KDD in Marketing with Genetic Fuzzy Systems | p. 225 |
| Knowledge Discovery in a Framework for Modelling with Words | p. 241 |
| Part IV Advanced Soft Computing Methods and Areas | |
| Swarm Intelligence Algorithms for Data Clustering | p. 279 |
| A Diffusion Framework for Dimensionality Reduction | p. 315 |
| Data Mining and Agent Technology: a fruitful symbiosis | p. 327 |
| Approximate Frequent Itemset Mining In the Presence of Random Noise | p. 363 |
| The Impact of Overfitting and Overgeneralization on the Classification Accuracy in Data Mining | p. 391 |
| Index | p. 433 |
