HUPM: Efficient High Utility Pattern Mining Algorithm for E-Business

Abstract

Utility pattern mining addresses the current common challenges of E-business by analysis of market behavior and customer trends of transactional data. However, it has some important limitations when it comes to analyzing customer transactions in any business as buying quantities are not considered into account. Thus, it leads to misappropriate analysis due to consideration of an item may only appear once or zero times in a transaction data and a weight of all item have given same importance. To address the above said confines, the problem of identification of frequent set of items as patterns has been defined in E business as High Utility Pattern Mining (HUPM). The focus of this paper is finding high utility patterns by using weighted utilization value of each product. This is implemented in two modules finding top k high utility patterns by constructing UP growth tree and TKU algorithm and finding top-k utilities in one phase approach with TKO algorithm to mine HUPs without any assumptions of minimum utility threshold. Experimental results show that the proposed algorithms take a smaller amount of computational cost, thus it shows more efficiency once compared with other present methods on standard data sets.