TaSPM: Targeted Sequential Pattern Mining

TL;DR

TaSPM introduces a generic targeted sequential pattern mining framework that enhances efficiency and reduces memory usage through pruning strategies, outperforming existing algorithms especially on large-scale datasets.

Contribution

The paper proposes a novel, general framework TaSPM for targeted sequential pattern mining, incorporating multiple pruning strategies for improved performance.

Findings

TaSPM achieves faster runtime compared to existing algorithms.

TaSPM uses less memory during mining processes.

Pruning strategies significantly improve efficiency on large datasets.

Abstract

Sequential pattern mining (SPM) is an important technique of pattern mining, which has many applications in reality. Although many efficient sequential pattern mining algorithms have been proposed, there are few studies can focus on target sequences. Targeted querying sequential patterns can not only reduce the number of sequences generated by SPM, but also improve the efficiency of users in performing pattern analysis. The current algorithms available on targeted sequence querying are based on specific scenarios and cannot be generalized to other applications. In this paper, we formulate the problem of targeted sequential pattern mining and propose a generic framework namely TaSPM, based on the fast CM-SPAM algorithm. What's more, to improve the efficiency of TaSPM on large-scale datasets and multiple-items-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in mining processes. Totally four pruning strategies are designed in TaSPM, and hence it can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conduct extensive experiments on different datasets to compare the existing SPM algorithms with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption.