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All Journal Media Teknik Sipil TEKNIK INFORMATIKA Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of ICT Research and Applications Indonesian Journal of Artificial Intelligence and Data Mining JURNAL NUSANTARA APLIKASI MANAJEMEN BISNIS Jurnal Telematika Building of Informatics, Technology and Science ACADEMICS IN ACTION Journal of Community Empowerment JISA (Jurnal Informatika dan Sains) International Journal of Advances in Data and Information Systems Jurnal Sains Indonesia IT For Society : Journal of Information Technology SENMEA
Tjong Wan Sen
Universitas Presiden
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Social Sciences Other

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Journal : Building of Informatics, Technology and Science

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Optimization in Time and Score using IID Algorithm for K-Modes Clustering Yulianti, Farah; Sen, Tjong Wan
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2791

Abstract

Nowadays, there are numerous methods for analyzing data, one of which is cluster analysis. Because most practical data in today's analysis contains categorical attributes, categorical data clustering has recently received a lot of attention. To cluster categorical data, unsupervised machine learning techniques, which used frequency-based method, such as K-Mode’s clustering are used. The K-Modes algorithm takes advantage of the differences between the data points (total mis-matches or dissimilarities). The lower the dissimilarities, the more similar the data points, and thus the better the cluster. This paper aims to improve K-Mode’s clustering performance by incorporating the intercluster and intracluster dissimilari-ty measure, or IID measure, into the K-Modes algorithm rather than just using the standard simple-matching method to increase the algorithm's accuracy and execution time. This combined algorithm improves accuracy and execution time of the K-Modes algorithm. As a result, this algorithm can be used as an alternative to better cluster categorical data.
Co-Authors Abdul Ghofir Andres, M Arry Akhmad Arman Bambang Riyanto Trilaksono ChungKiat, Ong Darien Daud Earlliawand Diky Djafar Sidik Eko Wahyudi Fahmi, Hasanul Faisal, Sutan Fauzi Ahmad Muda Forenziana, Jessica Hariyanto, Dian Tri Hartono Wijaya, Sony Hermanto Hermanto Lianzhai, Duan Linli, Peng Muhamad Ghofur Muhamad Muhaimin Rachma Oktari Riady, Sasmitoh Rahmad Rila Mandala Riyanto Adji Rosalina Rosalina Rusdianto Roestam Sahuri, Genta Sahuri Sinung Suakanto Siregar, Amril Mutoi Siti Aisyah Naili Mutia Sutisna, Nanang Ali Tukino, Tukino Yahya Suryana Yana Cahyana Yulianti, Farah