Investigating diversity of clustering methods: An empirical comparison

Roy Gelbard*, Orit Goldman, Israel Spiegler

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

180 Scopus citations


The paper aims to shed some light on the question why clustering algorithms, despite being quantitative and hence supposedly objective in nature, yield different and varied results. To do that, we took 10 common clustering algorithms and tested them over four known datasets, used in the literature as baselines with agreed upon clusters. One additional method, Binary-Positive, developed by our team, was added to the analysis. The results affirm the unpredictable nature of the clustering process, point to different assumptions taken by different methods. One conclusion of the study is to carefully choose the appropriate clustering method for any given application.

Original languageEnglish
Pages (from-to)155-166
Number of pages12
JournalData and Knowledge Engineering
Issue number1
StatePublished - Oct 2007


  • Binary-Positive data representation
  • Cluster analysis
  • Similarity


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