A conditional tree based novel algorithm for high utility itemset mining

In recent years, the problem of high utility pattern mining become one of the most important research area in data mining. The problem is challenging, due to non applicability of anti-monotone property. The existing high utility mining algorithm generates large number of candidate itemsets, which takes much time to find utility value of all candidate itemsets, especially for dense datasets. In this paper, a novel conditional high utility tree (CHUT) is proposed to compress transactional databases in two stages to reduce search space and a new algorithm called HU-Mine is proposed to mine complete set of high utility item sets. The proposed algorithm needs only two database scans in contrasts to many scans of existing algorithm. The results of the proposed work are compared with existing benchmark algorithm.