PERFICT: Perturbed Frequent Itemset Based Classification Technique

Abstract

This paper presents Perturbed Frequent Itemset based Classification Technique (PERFICT), a novel associative classification approach based on perturbed frequent itemsets. Most of the existing associative classifiers work well on transactional data where each record contains a set of boolean items. They are not very effective in general for relational data that typically contains real valued attributes. In PERFICT, we handle real attributes by treating items as (attribute, value) pairs, where the value is not the original one, but is perturbed by a small amount and is a range based value. We also propose our own similarity measure which captures the nature of real valued attributes and provide effective weights for the itemsets. The probabilistic contributions of different itemsets is taken into considerations during classification. Some of the applications where such a technique is useful are in signal classification, medical diagnosis and handwriting recognition. Experiments conducted on the UCI Repository datasets show that PERFICT is highly competitive in terms of accuracy in comparison with popular associative classification methods.