| Description |
xxi, 495 pages : illustrations ; 25 cm |
| Series |
Kluwer international series in engineering and computer science. ; SECS 458 |
|
Kluwer international series in engineering and computer science.
|
| Contents |
Ch. 1. Data Mining and Knowledge Discovery -- Ch. 2. Rough Sets -- Ch. 3. Fuzzy Sets -- Ch. 4. Bayesian Methods -- Ch. 5. Evolutionary Computing -- Ch. 6. Machine Learning -- Ch. 7. Neural Networks -- Ch. 8. Clustering -- Ch. 9. Preprocessing |
| Summary |
Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the 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 |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Data mining.
|
|
Database management.
|
| Author |
Pedrycz, Witold, 1953-
|
|
Świniarski, Roman.
|
| LC no. |
98029384 |
| ISBN |
0792382528 (alk. paper) |
|
