CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach

Abstract

Frequent pattern mining discovers patterns in transaction databases based only on the relative frequency of occurrence of items without considering their utility. For many real world applications, however, utility of itemsets based on cost, profit or revenue is of importance. The utility mining problem is to find itemsets that have higher utility than a user specified minimum. Unlike itemset support in frequent pattern mining, itemset utility does not have the anti-monotone property and so efficient high utility mining poses a greater challenge. Recent research on utility mining has been based on the candidate-generation-and-test approach which is suitable for sparse data sets with short patterns, but not feasible for dense data sets or long patterns. In this paper we propose a new algorithm called CTU-Mine that mines high utility itemsets using the pattern growth approach. We have tested our algorithm on several dense data sets, compared it with the recent algorithms and the results show that our algorithm works efficiently.