Investigating diversity of clustering methods: An empirical comparison

Roy Gelbard, Orit Goldman, Israel Spiegler

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)155-166
Number of pages12
JournalData and Knowledge Engineering
Volume63
Issue number1
DOIs
StatePublished - Oct 2007

Keywords

  • Binary-Positive data representation
  • Cluster analysis
  • Similarity

Fingerprint

Dive into the research topics of 'Investigating diversity of clustering methods: An empirical comparison'. Together they form a unique fingerprint.

Cite this