Investigating diversity of clustering methods: An empirical comparison

  • Authors:

  • Roy Gelbard;Orit Goldman;Israel Spiegler

  • Affiliations:

  • Information Systems Program, Graduate School of Business Administration, Bar-Ilan University, Ramat-Gan 52900, Israel;Technology and Information Systems Program, The Recanati Graduate School of Business Administration, Tel Aviv University, Tel Aviv 69978, Israel;Technology and Information Systems Program, The Recanati Graduate School of Business Administration, Tel Aviv University, Tel Aviv 69978, Israel

  • Venue:
  • Data & Knowledge Engineering
  • Year:
  • 2007

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Abstract

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.