article

A simple and fast algorithm for K-medoids clustering

Authors:

Hae-Sang Park,

Chi-Hyuck JunAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 36, Issue 2

Pages 3336 - 3341

https://doi.org/10.1016/j.eswa.2008.01.039

Published: 01 March 2009 Publication History

Abstract

This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for selecting initial medoids. The proposed algorithm calculates the distance matrix once and uses it for finding new medoids at every iterative step. To evaluate the proposed algorithm, we use some real and artificial data sets and compare with the results of other algorithms in terms of the adjusted Rand index. Experimental results show that the proposed algorithm takes a significantly reduced time in computation with comparable performance against the partitioning around medoids.

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Chang YTanin ECong GJensen CQi J(2024)Trajectory Similarity Measurement: An Efficiency PerspectiveProceedings of the VLDB Endowment10.14778/3665844.366585817:9(2293-2306)Online publication date: 1-May-2024
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Zeng ZSun SLi TYin JShen YHuang Q(2024)Exploring the topic evolution of Dunhuang murals through image classificationJournal of Information Science10.1177/0165551522107433650:1(35-52)Online publication date: 1-Feb-2024
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Index Terms

A simple and fast algorithm for K-medoids clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Expert systems

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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 36, Issue 2

March, 2009

3191 pages

ISSN:0957-4174

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Pergamon Press, Inc.

United States

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Published: 01 March 2009

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Chang YTanin ECong GJensen CQi J(2024)Trajectory Similarity Measurement: An Efficiency PerspectiveProceedings of the VLDB Endowment10.14778/3665844.366585817:9(2293-2306)Online publication date: 1-May-2024
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