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Building fast and accurate classifiers for large-scale databases is an important task in data mining. There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. In this paper, the problem of producing rules with multiple labels is investigated, and we propose a multi-class, multilabel associative classification approach (MMAC). In addition, four measures are presented in this paper fordoi:10.1007/s10115-005-0213-x fatcat:o5v6vnaegncovbefj6kddfaw4m