Non-disjoint Cluster Analysis with Non-uniform Density

  • Chiheb-Eddine Ben N’Cir
  • Nadia Essoussi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)


Non-disjoint clustering, also referred to as overlapping clustering, is a challenging issue in clustering which allows an observation to belong to more than one cluster. Several overlapping methods were proposed to solve this issue. Although the effectiveness of these methods to build non-disjoint partitioning, they usually fail when clusters have different densities. In order to detect overlapping clusters with uneven densities, we propose two clustering methods based on a new optimized criterion that incorporates the distance variation in a cluster to regularize the distance between a data point and the cluster representative. Experiments performed on simulated data and real world benchmarks show that proposed methods have better performance, compared to existing ones, when clusters have different densities.


Overlapping Clustering Clusters with Different Densities Overlapping k-means Distance Variation 


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Chiheb-Eddine Ben N’Cir
    • 1
  • Nadia Essoussi
    • 1
  1. 1.LARODEC, ISG of TunisUniversity of TunisTunisia

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