Mining High Quality Patterns Using Multi-Objective Evolutionary Algorithm


The term "pattern mining," or PM for short, refers to the process of extracting from data patterns that are of interest to users. On the other hand, the vast majority of studies have only focused on a single pattern, such as a frequent pattern or a high-utility pattern. It is difficult for single-objective PM methods to accommodate the increasingly varied requirements of their users as a result of the continuous demand placed on businesses operating in a variety of industries. A multi-objective problem model for high quality pattern mining (HQPM) is proposed in this article. The goals of the model are support, occupancy, and utility. It is proposed that an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) be used in order to solve the proposed three-objective problem as effectively as possible. In order to guarantee that the population is effectively distributed in the feasible solution space, two distinct types of population initialization strategies are designed. These strategies can be used. An auxiliary tool that takes into account the properties of the model is proposed as a means of speeding up the algorithm's convergence. The proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and have a high utility in the transaction datasets, while at the same time being relatively complete, as shown by experimental results on real-world datasets. This is demonstrated by the fact that the model can discover patterns that are both frequently occurring and have a high utility in the datasets. When compared with MOEA-based HQPM algorithms that are considered to be state-of-the-art, MOEA-PM has superior performance in terms of efficiency, quality, and convergence speed.

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