Efficient Algorithm for Mining High Utility Itemsets

Abstract

Utility-based data mining is a new research area interested in all types of utility factors in data mining processes and targeted at incorporating utility considerations in both predictive and descriptive data mining tasks. High utility itemset mining is a research area of utilitybased descriptive data mining, aimed at finding itemsets that contribute most to the total utility. A specialized form of high utility itemset mining is utility-frequent itemset mining, which – in addition to subjectively defined utility – also takes into account itemset frequencies. This paper presents a novel efficient algorithm FUFM (Fast Utility-Frequent Mining) which finds all utility-frequent itemsets within the given utility and support constraints threshold. It is faster and simpler than the original 2P-UF algorithm (2 Phase Utility-Frequent), as it is based on efficient methods for frequent itemset mining. Experimental evaluation on artificial datasets show that, in contrast with 2P-UF,this algorithm can also be applied to mine large databases..