FDHUP: Fast algorithm for mining discriminative high utility patterns

Abstract

Recently, high utility pattern mining (HUPM) has been extensively studied. Many approaches for HUPM have been proposed in recent years, but most of them aim at mining HUPs without any consideration for their frequency. This has the major drawback that any combination of a low utility item with a very high utility pattern is regarded as a HUP, even if this combination has low affinity and contains items that rarely co-occur. Thus, frequency should be a key criterion to select HUPs. To address this issue, and derive high utility interesting patterns (HUIPs) with strong frequency affinity, the HUIPM algorithm was proposed. However, it recursively constructs a series of conditional trees to produce candidates and then derive the HUIPs. This procedure is time-consuming and may lead to a combinatorial explosion when the minimum utility threshold is set relatively low. In this paper, an efficient algorithm named fast algorithm for mining discriminative high utility patterns (DHUPs) with strong frequency affinity (FDHUP) is proposed to efficiently discover DHUPs by considering both the utility and frequency affinity constraints. Two compact structures named EI-table and FU-tree and three pruning strategies are introduced in the proposed algorithm to reduce the search space, and efficiently and effectively discover DHUPs. An extensive experimental study shows that the proposed FDHUP algorithm considerably outperforms the state-of-the-art HUIPM algorithm in terms of execution time, memory consumption, and scalability.

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