On-shelf Utility Mining of Sequence Data

TL;DR

This paper introduces methods for on-shelf utility mining of sequence data, addressing bias in traditional utility mining by considering temporal availability of items, and demonstrates improved efficiency and effectiveness over existing algorithms.

Contribution

The paper proposes two novel algorithms, OSUMS and OSUMS+, for on-shelf high-utility sequential pattern mining, incorporating strategies to reduce search space and improve computational efficiency.

Findings

OSUMS outperforms existing algorithms in accuracy.

OSUMS+ offers higher efficiency and wider applicability.

Experimental results validate the effectiveness of the proposed methods.

Abstract

Utility mining has emerged as an important and interesting topic owing to its wide application and considerable popularity. However, conventional utility mining methods have a bias toward items that have longer on-shelf time as they have a greater chance to generate a high utility. To eliminate the bias, the problem of on-shelf utility mining (OSUM) is introduced. In this paper, we focus on the task of OSUM of sequence data, where the sequential database is divided into several partitions according to time periods and items are associated with utilities and several on-shelf time periods. To address the problem, we propose two methods, OSUM of sequence data (OSUMS) and OSUMS+, to extract on-shelf high-utility sequential patterns. For further efficiency, we also designed several strategies to reduce the search space and avoid redundant calculation with two upper bounds time prefix extension utility (TPEU) and time reduced sequence utility (TRSU). In addition, two novel data structures were developed for facilitating the calculation of upper bounds and utilities. Substantial experimental results on certain real and synthetic datasets show that the two methods outperform the state-of-the-art algorithm. In conclusion, OSUMS may consume a large amount of memory and is unsuitable for cases with limited memory, while OSUMS+ has wider real-life applications owing to its high efficiency.