TKUS: Mining Top-K High-Utility Sequential Patterns

TL;DR

This paper introduces TKUS, a novel algorithm for top-k high-utility sequential pattern mining that efficiently finds the most useful patterns without needing a predefined utility threshold, outperforming previous methods.

Contribution

The paper formulates the top-k HUSPM problem and proposes TKUS, an efficient algorithm with new strategies to improve search space reduction and scalability.

Findings

TKUS outperforms existing algorithms in efficiency.

The proposed strategies significantly reduce search space.

Experimental results validate the effectiveness of TKUS.

Abstract

High-utility sequential pattern mining (HUSPM) has recently emerged as a focus of intense research interest. The main task of HUSPM is to find all subsequences, within a quantitative sequential database, that have high utility with respect to a user-defined minimum utility threshold. However, it is difficult to specify the minimum utility threshold, especially when database features, which are invisible in most cases, are not understood. To handle this problem, top-k HUSPM was proposed. Up to now, only very preliminary work has been conducted to capture top-k HUSPs, and existing strategies require improvement in terms of running time, memory consumption, unpromising candidate filtering, and scalability. Moreover, no systematic problem statement has been defined. In this paper, we formulate the problem of top-k HUSPM and propose a novel algorithm called TKUS. To improve efficiency, TKUS adopts a projection and local search mechanism and employs several schemes, including the Sequence Utility Raising, Terminate Descendants Early, and Eliminate Unpromising Items strategies, which allow it to greatly reduce the search space. Finally, experimental results demonstrate that TKUS can achieve sufficiently good top-k HUSPM performance compared to state-of-the-art algorithm TKHUS-Span.