A new approach to classification based on association rule mining

Classification is one of the key issues in the fields of decision sciences and knowledge discovery. This paper presents a new approach for constructing a classifier, based on an extended association rule mining technique in the context of classification. The characteristic of this approach is threefold: first, applying the information gain measure to the generation of candidate itemsets; second, integrating the process of frequent itemsets generation with the process of rule generation; third, incorporating strategies for avoiding rule redundancy and conflicts into the mining process. The corresponding mining algorithm proposed, namely GARC (Gain based Association Rule Classification), produces a classifier with satisfactory classification accuracy, compared with other classifiers (e.g., C4.5, CBA, SVM, NN). Moreover, in terms of association rule based classification, GARC could filter out many candidate itemsets in the generation process, resulting in a much smaller set of rules than that of CBA.