Clustering by Integrating Multi-objective Optimization with Weighted K-Means and Validity Analysis

  • Tansel Özyer
  • Reda Alhajj
  • Ken Barker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


This paper presents a clustering approach that integrates multi-objective optimization, weighted k-means and validity analysis in an iterative process to automatically estimate the number of clusters, and then partition the whole given data to produce the most natural clustering. The proposed approach has been tested on real-life dataset; results of both weighted and unweighed k-means are reported to demonstrate applicability and effectiveness of the proposed approach.


Validity Analysis Validity Index Cluster Validity Object Pair Natural Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tansel Özyer
    • 1
    • 3
  • Reda Alhajj
    • 1
    • 2
  • Ken Barker
    • 1
  1. 1.Dept. of Computer ScienceUniversity of CalgaryCalgary, AlbertaCanada
  2. 2.Dept. of Computer ScienceGlobal UniversityBeirutLebanon
  3. 3.Dept. of Comp. Eng.TOBB Economics & Technology UniversityAnkaraTurkey

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