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Editorial Reviews
Review
Product Description
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.
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From the reviews:
"In the snappily-titled Data Mining and Knowledge Discovery with Evolutionary Algorithms, leading researcher Alex A Freitas introduces both data mining and evolutionary algorithms. The aim is to introduce and address the key challenges to a high level of detail. With an understanding gleaned from this book, and source code available freely on the web, the world of data mining is your oyster." (Application Development Advisor, January/February, 2003)
Product Description
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.
See all Editorial Reviews
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Inside This Book Citations: This book cites 46 books | 26 books that cite this book Explore: Citations | Books on Related Topics | Concordance | Text Stats Key Phrases - SIPs: being fuzzified, data instance satisfies, data being mined, attribute construction method, data set being mined (more) Key Phrases - CAPs: Morgan Kaufmann, International Conference, Lecture Notes, Annual Conference, European Conference (more) Browse Sample Pages: Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover | Surprise Me! |
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Inside This Book
(learn more)
First Sentence:
Nowadays there is a huge amount of data stored in real-world databases, and this amount continues to grow fast. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
being fuzzified, data instance satisfies, data being mined, attribute construction method, data set being mined, candidate attribute subset, rule induction paradigm, given rule antecedent, same rule consequent, constructing new attributes, predictor attributes, new boolean attributes, rule comprehensibility, attribute selection methods, given data instance, evolving decision trees, collapse mutation, individual encoding, rule interestingness measures, data preparation task, data mining paradigms, wrapper approach, unseen test set, rule induction algorithms, classification accuracy rate Key Phrases - Capitalized Phrases (CAPs): (learn more)
Morgan Kaufmann, International Conference, Lecture Notes, Annual Conference, European Conference, Proceedings of the Congress,
Nowadays there is a huge amount of data stored in real-world databases, and this amount continues to grow fast. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
being fuzzified, data instance satisfies, data being mined, attribute construction method, data set being mined, candidate attribute subset, rule induction paradigm, given rule antecedent, same rule consequent, constructing new attributes, predictor attributes, new boolean attributes, rule comprehensibility, attribute selection methods, given data instance, evolving decision trees, collapse mutation, individual encoding, rule interestingness measures, data preparation task, data mining paradigms, wrapper approach, unseen test set, rule induction algorithms, classification accuracy rate Key Phrases - Capitalized Phrases (CAPs): (learn more)
Morgan Kaufmann, International Conference, Lecture Notes, Annual Conference, European Conference, Proceedings of the Congress,