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Data mining and knowledge discovery with evolutionary algorithms
This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an area of active research.In general, data mining consists of extracting knowledge from dat...
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| Format: | Printed Book |
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New York
Springer
2002
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| Edition: | 1st. Indian Reprint . |
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| LEADER | 02076nam a2200181Ia 4500 | ||
|---|---|---|---|
| 999 | |c 27158 |d 27158 | ||
| 020 | |a 9788181287915 | ||
| 082 | |a 006.3 FRE-D | ||
| 100 | |a Alex A. Freitas | ||
| 245 | |a Data mining and knowledge discovery with evolutionary algorithms | ||
| 250 | |a 1st. Indian Reprint . | ||
| 260 | |a New York |b Springer |c 2002 | ||
| 300 | |a xiv, 264 pages : illustrations ; 24 cm. | ||
| 505 | |a 1. Introduction -- 2. Data Mining Tasks and Concepts -- 3. Data Mining Paradigms -- 4. Data Preparation -- 5. Basic Concepts of Evolutionary Algorithms -- 6. Genetic Algorithms for Rule Discovery -- 7. Genetic Programming for Rule Discovery -- 8. Evolutionary Algorithms for Clustering -- 9. Evolutionary Algorithms for Data Preparation -- 10. Evolutionary Algorithms for Discovering Fuzzy Rules -- 11. Scaling up Evolutionary Algorithms for Large Data Sets -- 12. Conclusions and Research Directions. | ||
| 520 | |a This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an area of active research.In general, data mining consists of extracting knowledge from data. In this book we particularly emphasize the importance of discovering comprehensible, interesting knowledge, which is potentially useful for the reader for intelligent decision making.In a nutshell, the motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search. | ||
| 650 | |a Data mining. Database searching. Computer algorithms. | ||
| 942 | |c BK | ||
| 952 | |0 0 |1 0 |4 0 |6 006_300000000000000_FRED |7 0 |9 29742 |a DCB |b DCB |e MBC-2002/07 |g 595.00 |l 0 |o 006.3 FRE-D |p DCB538 |r 2015-09-01 |w 2015-09-01 |y BK | ||