Mining discriminative high utility patterns

Abstract

Recently, many approaches for high utility pattern mining (HUPM) have been proposed, but most of them aim at mining highutility patterns (HUPs) instead of frequent ones. The major drawback is that any combination of a low-utility item with a very high utility pattern is regarded as a HUP, even if this combination is infrequent and contains items that rarely co-occur. Thus, the HUIPM algorithm was proposed to derive high utility interesting patterns (HUIPs) with strong frequency affinity. However, it recursively constructs a series of conditional trees to produce candidates, and then derive the HUIPs. It is time-consuming and may lead to a combinatorial explosion. In this paper, a Fast algorithm for mining Discriminative High Utility Patterns with strong frequency affinity (FDHUP) is proposed by considering both the utility and frequency affinity constraints. Two compact structures named EI-table and FU-table, and two pruning strategies are designed to reduce the search space, and efficiently and effectively discover DHUPs. Experimental results show that the proposed FDHUP algorithm considerably outperforms the state-of-the-art HUIPM algorithm in all datasets.