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Perbandingan pengukuran jarak Euclidean dan Gower pada klaster k-medoids

Comparison analysis of Euclidean and Gower distance measures on k-medoids cluster

Department of Informatics, Universitas Singaperbangsa Karawang. Jl. H.S. Ronggowaluyo, Teluk Jambe Timur, Karawang, Jawa Barat 41361, Indonesia

Received: 8 May 2020; Revised: 21 Oct 2020; Accepted: 24 Oct 2020; Published: 31 Jan 2021; Available online: 27 Oct 2020.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
K-medoids clustering uses distance measurement to find and classify data that have similarities and inequalities. The distance measurement method selection can affect the clustering performance for a dataset. Several studies use the Euclidean and Gower distance as measurement methods in numerical data clustering. This study aims to compare the performance of the k-medoids clustering on a numerical dataset using the Euclidean and Gower distance. This study used seven numerical datasets and Silhouette, Dunn, and Connectivity indexes in the clustering evaluation. The Euclidean distance is superior in two values of Silhouette and Connectivity indexes so that Euclidean has a good data grouping structure, while the Gower is superior in Dunn index showing that the Gower has better cluster separation compared to Euclidean. This study shows that the Euclidean distance is superior to the Gower in applying the k-medoids algorithm with a numeric dataset.
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Keywords: clustering; data mining; Euclidean; Gower; k-medoids
Funding: Universitas Singaperbangsa Karawang

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