An Efficient Data Structure for Fast Mining High Utility Itemsets

Abstract: High utility itemset mining has emerged to be an important research issue in data mining since it has a wide range of real life applications. Although a number of algorithms have been proposed in recent years, there seems to be still a lack of efficient algorithms since these algorithms suffer from either the problem of low efficiency of calculating candidates’ utilities or the problem of generating huge number of candidates. In this paper, we propose a novel data structure called PUNlist (PU-tree-Node list), which maintains both the utility information about an itemset and utility upper bound for facilitating the processing of mining high utility itemsets. Based on PUN-lists, we present a method, called MIP (Mining high utility Itemset using PUN-Lists), for fast mining high utility itemsets. The efficiency of MIP is achieved with three techniques. First, itemsets are represented by a highly condensed data structure, PUN-list, which avoids costly, repeatedly utility computation. Second, the utility of an itemset can be efficiently calculated by scanning the PUN-list of the itemset and the PUN-lists of long itemsets can be fast constructed by the PUN-lists of short itemsets. Third, by employing the utility upper bound lying in the PUN-lists as the pruning strategy, MIP directly discovers high utility itemsets from the search space, called set-enumeration tree, without generating numerous candidates. Extensive experiments on various synthetic and real datasets show that PUN-list is very effective since MIP is at least an order of magnitude faster than recently reported state-of-the-art algorithms on average.