Itemset Based Sequence Classification

Abstract

Sequence classification is an important task in data mining. We address the problem of sequence classification using rules composed of interesting itemsets found in a dataset of labelled sequences and accompanying class labels. We measure the interestingness of an itemset in a given class of sequences by combining the cohesion and the support of the itemset. We use the discovered itemsets to generate confident classification rules, and present two different ways of building a classifier. The first classifier is based on the CBA (Classification based on associations) method, but we use a new ranking strategy for the generated rules, achieving better results. The second classifier ranks the rules by first measuring their value specific to the new data object. Experimental results show that our classifiers outperform existing comparable classifiers in terms of accuracy and stability, while maintaining a computational advantage over sequential pattern based classification.