Mining High Quality Patterns Using Multi-Objective Evolutionary Algorithm

Pattern mining (PM) refers to the process of discovering patterns of interest to users from data. However, most studies have considered only one pattern, such as frequent pattern or high-utility pattern. With the continuous requirement of businesses in various industries, the single-objective PM met...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 34; no. 8; p. 1
Main Authors: Fang, Wei, Zhang, Qiang, Sun, Jun, Wu, Xiao-Jun
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Convergence
Data mining
Datasets
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Itemsets
multi-objective evolutionary algorithm
occupancy
Optimization
Pattern analysis
Pattern mining
Sociology
Solution space
Statistics
support
utility
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary:Pattern mining (PM) refers to the process of discovering patterns of interest to users from data. However, most studies have considered only one pattern, such as frequent pattern or high-utility pattern. With the continuous requirement of businesses in various industries, the single-objective PM methods are difficult to meet the increasingly diverse needs of users. In this paper, a multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed. Two kinds of population initialization strategies are designed, which is used to ensure the population is effectively distributed in the feasible solution space. By taking the properties of the model into consideration, an auxiliary tool is proposed to accelerate the convergence of the algorithm. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. Compared with the state-of-the-art MOEA-based HQPM algorithms, MOEA-PM has better performance in terms of efficiency, quality, and convergence speed.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2020.3033519