A heuristic for non-convex variance-based clustering criteria

Rodrigo F. Toso, Casimir A. Kulikowski, Ilya B. Muchnik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We address the clustering problem in the context of exploratory data analysis, where data sets are investigated under different and desirably contrasting perspectives. In this scenario where, for flexibility, solutions are evaluated by criterion functions, we introduce and evaluate a generalized and efficient version of the incremental one-by-one clustering algorithm of MacQueen (1967). Unlike the widely adopted two-phase algorithm developed by Lloyd (1957), our approach does not rely on the gradient of the criterion function being optimized, offering the key advantage of being able to deal with non-convex criteria. After an extensive experimental analysis using real-world data sets with a more flexible, non-convex criterion function, we obtained results that are considerably better than those produced with the k-means criterion, making our algorithm an invaluable tool for exploratory clustering applications.

Original languageEnglish (US)
Title of host publicationExperimental Algorithms - 11th International Symposium, SEA 2012, Proceedings
Pages381-392
Number of pages12
DOIs
StatePublished - 2012
Event11th International Symposium on Experimental Algorithms SEA 2012 - Bordeaux, France
Duration: Jun 7 2012Jun 9 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7276 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Symposium on Experimental Algorithms SEA 2012
CountryFrance
CityBordeaux
Period6/7/126/9/12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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