Data Mining and Knowledge Discovery with Evolutionary AlgorithmsThis book addresses the integration of two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increas ingly popular in the last few years, and their integration is currently an area of active research. In essence, data mining consists of extracting valid, comprehensible, and in teresting knowledge from data. Data mining is actually an interdisciplinary field, since there are many kinds of methods that can be used to extract knowledge from data. Arguably, data mining mainly uses methods from machine learning (a branch of artificial intelligence) and statistics (including statistical pattern recog nition). Our discussion of data mining and evolutionary algorithms is primarily based on machine learning concepts and principles. In particular, in this book we emphasize the importance of discovering comprehensible, interesting knowledge, which the user can potentially use to make intelligent decisions. 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 (rules or another form of knowl edge representation). 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. |
Contents
1 | |
References | 10 |
Evolutionary Algorithms for Clustering 165 | 33 |
References | 40 |
Data Mining Paradigms | 45 |
References | 60 |
References | 76 |
References | 103 |
References | 134 |
References | 161 |
Evolutionary Algorithms for Data Preparation | 179 |
References | 201 |
References | 230 |
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Other editions - View all
Data Mining and Knowledge Discovery with Evolutionary Algorithms Alex A. Freitas Limited preview - 2002 |
Data Mining and Knowledge Discovery with Evolutionary Algorithms Alex A. Freitas No preview available - 2012 |
Data Mining and Knowledge Discovery with Evolutionary Algorithms Alex A. Freitas No preview available - 2014 |
Common terms and phrases
A.A. Freitas application domain Artificial Intelligence Attr attribute construction attribute interaction attribute selection attribute subset attribute values basic idea candidate rule candidate solution categorical attributes classification algorithm clustering confusion matrix continuous attributes corresponding crossover crossover operators data being mined data instances data mining algorithm data mining task data set data type database decision tree decision-tree building discovered rules evolutionary algorithms Evolutionary Computation example fitness evaluation fitness function function set fuzzy rules genes Genetic Algorithms genetic operators Genetic Programming genotype goal attribute individual encoding individual representation inductive bias kind Knowledge Discovery Machine Learning measure membership functions Morgan Kaufmann number of data original attributes paradigm parallel partitioning performance population prediction rules predictive accuracy predictor attributes problem Proceedings processors Quinlan rithm rule antecedent rule conditions rule consequent rule discovery rule induction rule induction algorithms rule set Salary shown in Figure subsection test set tion training set wrapper approach