Open Access

ARTICLE

High Utility Periodic Frequent Pattern Mining in Multiple Sequences

Chien-Ming Chen¹, Zhenzhou Zhang¹, Jimmy Ming-Tai Wu¹, Kuruva Lakshmanna^2,*

1 College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China
2 Department of Information Technology, Vellore Institute of Technology, Vellore, 632014, India

* Corresponding Author: Kuruva Lakshmanna. Email:

(This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)

Computer Modeling in Engineering & Sciences 2023, 137(1), 733-759. https://doi.org/10.32604/cmes.2023.027463

Received 31 October 2022; Accepted 03 January 2023; Issue published 23 April 2023

Abstract

Periodic pattern mining has become a popular research subject in recent years; this approach involves the discovery of frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic pattern mining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodic patterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequences is more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences is important. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To address existing problems, three new measures are defined: the utility, high support, and high-utility period sequence ratios. Further, a new upper bound, , and two new pruning properties were proposed. MHUPFPS uses a newly defined HUPFPS-list structure to significantly accelerate the reduction of the search space and improve the overall performance of the algorithm. Furthermore, the proposed algorithm is evaluated using several datasets. The experimental results indicate that the algorithm is accurate and effective in filtering several non-high-utility periodic frequent patterns.

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Keywords

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