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Computer Science - Security and Cryptology | Data Mining Methods for Knowledge Discovery

Data Mining Methods for Knowledge Discovery

Cios, Krzysztof J., Pedrycz, Witold, Swiniarski, Roman W.

1998, XXI, 495 p.

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  • About this book

Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. The book then goes on to thoroughly discuss these methods in the setting of the overall process of knowledge discovery. Numerous illustrative examples and experimental findings are also included. Each chapter comes with an extensive bibliography.
Data Mining Methods for Knowledge Discovery is intended for senior undergraduate and graduate students, as well as a broad audience of professionals in computer and information sciences, medical informatics, and business information systems.

Content Level » Research

Keywords » algorithms - data mining - evolution - evolutionary computation - fuzzy - fuzzy sets - genetic algorithms - information - information system - knowledge - knowledge discovery - learning - machine learning - networks - neural networks

Related subjects » Artificial Intelligence - Business Information Systems - Database Management & Information Retrieval - Security and Cryptology

Table of contents 

Foreword. Preface. 1. Data Mining and Knowledge Discovery. 2. Rough Sets. 3. Fuzzy Sets. 4. Bayesian Methods. 5. Evolutionary Computing. 6. Machine Learning. 7. Neural Networks. 8. Clustering. 9. Preprocessing. Index.

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