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All Journal International Journal of Electrical and Computer Engineering
Nazratul Naziah Mohd Muhait
Universiti Malaysia Terengganu
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Performance analysis in text clustering using k-means and k-medoids algorithms for Malay crime documents Rosmayati Mohemad; Nazratul Naziah Mohd Muhait; Noor Maizura Mohamad Noor; Zulaiha Ali Othman
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5014-5026

Abstract

Few studies on text clustering for the Malay language have been conducted due to some limitations that need to be addressed. The purpose of this article is to compare the two clustering algorithms of k-means and k-medoids using Euclidean distance similarity to determine which method is the best for clustering documents. Both algorithms are applied to 1000 documents pertaining to housebreaking crimes involving a variety of different modus operandi. Comparability results indicate that the k-means algorithm performed the best at clustering the relevant documents, with a 78% accuracy rate. K-means clustering also achieves the best performance for cluster evaluation when comparing the average within-cluster distance to the k-medoids algorithm. However, k-medoids perform exceptionally well on the Davis Bouldin index (DBI). Furthermore, the accuracy of k-means is dependent on the number of initial clusters, where the appropriate cluster number can be determined using the elbow method.
Co-Authors Noor Maizura Mohamad Noor Rosmayati Mohemad Zulaiha Ali Othman