Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution

Author:

Tahyudin Imam,Nambo Hidetaka

Abstract

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>Particle Swarm Optimization (PSO) has been applied to solve optimization problems in various fields, such as Association Rule Mining (ARM) of numerical problems. However, PSO often becomes trapped in local optima. Consequently, the results do not represent the overall optimum solutions. To address this limitation, this study aims to combine PSO with the Cauchy distribution (PARCD), which is expected to increase the global optimal value of the expanded search space. Furthermore, this study uses multiple objective functions, i.e., support, confidence, comprehensibility, interestingness and amplitude. In addition, the proposed method was evaluated using benchmark datasets, such as the Quake, Basket ball, Body fat, Pollution, and Bolt datasets. Evaluation results were compared to the results obtained by previous studies. The results indicate that the overall values of the objective functions obtained using the proposed PARCD approach are satisfactory.</p></div></div></div>

Publisher

Institute of Advanced Engineering and Science

Subject

Electrical and Electronic Engineering,General Computer Science

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data;IEEE Access;2024

2. Machine Learning Analysis for Temperature Classification using Bioelectric Potential of Plant;2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE);2022-12-13

3. A Brief Overview of Swarm Intelligence-Based Algorithms for Numerical Association Rule Mining;Springer Tracts in Nature-Inspired Computing;2021

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