Mining compact high utility sequential patterns

TL;DR

This paper introduces CHUSP, an algorithm for efficiently mining a compact set of high utility sequential patterns that are closed and frequent, reducing redundancy and computational complexity in sequence data analysis.

Contribution

The paper proposes a novel algorithm called CHUSP that efficiently mines closed high utility sequential patterns using pruning strategies and a specialized data structure.

Findings

CHUSP significantly reduces execution time compared to baseline methods.

The algorithm effectively minimizes memory usage during pattern mining.

It successfully discovers a compact set of patterns across various datasets.

Abstract

High utility sequential pattern mining (HUSPM) aims to mine all patterns that yield a high utility (profit) in a sequence dataset. HUSPM is useful for several applications such as market basket analysis, marketing, and website clickstream analysis. In these applications, users may also consider high utility patterns frequently appearing in the dataset to obtain more fruitful information. However, this task is high computation since algorithms may generate a combinatorial explosive number of candidates that may be redundant or of low importance. To reduce complexity and obtain a compact set of frequent high utility sequential patterns (FHUSPs), this paper proposes an algorithm named CHUSP for mining closed frequent high utility sequential patterns (CHUSPs). Such patterns keep a concise representation while preserving the same expressive power of the complete set of FHUSPs. The proposed algorithm relies on a CHUS data structure to maintain information during mining. It uses three pruning strategies to eliminate early low-utility and non-frequent patterns, thereby reducing the search space. An extensive experimental evaluation was performed on six real-life datasets to evaluate the performance of CHUSP in terms of execution time, memory usage, and the number of generated patterns. Experimental results show that CHUSP can efficiently discover the compact set of CHUSPs under different user-defined thresholds.