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Partial classification popularly known as nugget discovery comes underdescriptive knowledge discovery. It involves mining rules for a target class ofinterest. Classification "If-Then" rules are the most sought out by decisionmakers since they are the most comprehensible form of knowledge mined by datamining techniques. The rules have certain properties namely the rule metricswhich are used to evaluate them. Mining rules with user specified propertiescan be considered as a multi-objective optimization problem since the ruleshave to satisfy more than one property to be used by the user. Culturalalgorithm (CA) with its knowledge sources have been used in solving manyoptimization problems. However research gap exists in using cultural algorithmfor multi-objective optimization of rules. In the current study amulti-objective cultural algorithm is proposed for partial classification.Results of experiments on benchmark data sets reveal good performance.

Nugget Discovery with a Multi-objective Cultural Algorithm