Comparative study of document clustering algorithms

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Text clustering is a data mining technique that is becoming more important in present studies. Document clustering makes use of text clustering to divide documents according to the various topics. The choice of words in document clustering is important to ensure that the document can be classified correctly. Three different methods of clustering which are hierarchical clustering, k-means and k-medoids are used and compared in this study in order to identify the best method which produce the best result in document clustering. The three methods are applied on 60 sports articles involving four different types of sports. The k-medoids clustering produced the worst result while k-means clustering is found to be more sensitive towards general words. Therefore, the method of hierarchical clustering is deemed more stable to produce a meaningful result in document clustering analysis.

Original languageEnglish
Pages (from-to)246-251
Number of pages6
JournalInternational Journal of Engineering and Technology(UAE)
Volume7
Issue number4
DOIs
Publication statusPublished - 1 Jan 2018

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Sports
Clustering algorithms
Cluster Analysis
Data mining
Data Mining

Keywords

  • Document clustering
  • Hierarchical clustering
  • K-means
  • K-medoids
  • Text mining

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science (miscellaneous)
  • Environmental Engineering
  • Chemical Engineering(all)
  • Engineering(all)
  • Hardware and Architecture

Cite this

Comparative study of document clustering algorithms. / Mohd Ariff, Noratiqah; Abu Bakar, Mohd Aftar; Rahmad, M. I.

In: International Journal of Engineering and Technology(UAE), Vol. 7, No. 4, 01.01.2018, p. 246-251.

Research output: Contribution to journalArticle

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