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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Pro Bisnis Telematika Indonesian Journal of Electrical Engineering and Computer Science International Journal of Engineering, Science and Information Technology
Imam Tahyudin
STMIK AMIKOM Purwokerto
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Journal : International Journal of Electrical and Computer Engineering

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Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution Imam Tahyudin; Hidetaka Nambo
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1128.612 KB) | DOI: 10.11591/ijece.v9i2.pp1359-1373

Abstract

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.
Co-Authors Adinda Rifi Yanti Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Dwi Puspa Sari Fahmy Putra Nahri Utama Haviluddin Haviluddin Hidetaka Nambo Ita Yulianti M. Syaiful Amin Mohammad imron Muhammad Yanuar Firdaus Nur Atikah Fitriyanti Nur Dewiyanti Nurfaizah Nurfaizah Sefitriani Khasanah Siti Alvi Solikhatin