Abstract
Classification Rule Mining (CRM) is a data mining technique for discovering important classification rules from large dataset. This work presents an efficient genetic algorithm for discovering significant IF-THEN rules from a given dataset. The proposed algorithm consists of two main steps. First step generates set of classification rules and the second step deletes the weak rules and selects only the significant rules. Since weak rules are deleted and significant rules are selected, the proposed algorithm can be considered as knowledge acquisition tool for classification problems. Experimental results are presented to demonstrate the contribution of the proposed algorithm for discovering the significant rules. General Terms Knowledge Discovery in Databases (KDD), Data Mining, Genetic Algorithm, Machine Learning, Pattern Recognition.
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