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)

Abstract

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.

Keywords

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Snoek, C.G.M., Worring, M., van Gemert, J.C., Geusebroek, J.M., Smeulders, A.W.M.: The challenge problem for automated detection of 101 semantic concepts in multimedia. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, MULTIMEDIA 2006, pp. 421–430. ACM, New York (2006)Google Scholar
  2. 2.
    Wieczorkowska, A., Synak, P., Ras, Z.: Multi-label classification of emotions in music. In: Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol. 35, pp. 307–315 (2006)Google Scholar
  3. 3.
    MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proc. of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)Google Scholar
  4. 4.
    Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recognition Letters 31(8), 651–666 (2010)CrossRefGoogle Scholar
  5. 5.
    Bezdek, J.C., Ehrlich, R., Full, W.: Fcm: The fuzzy c-means clustering algorithm. Computers Amp; Geosciences 10(23), 191–203 (1984)CrossRefGoogle Scholar
  6. 6.
    Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems 1, 98–110 (1993)CrossRefGoogle Scholar
  7. 7.
    Masson, M.H., Denux, T.: Ecm: An evidential version of the fuzzy c-means algorithm. Pattern Recognition 41(4), 1384–1397 (2008)CrossRefMATHGoogle Scholar
  8. 8.
    Qinand, A.K., Suganthan, P.N.: Kernel neural gas algorithms with application to cluster analysis. In: Int. Conf. on Pattern Recognition, vol. 4, pp. 617–620 (2004)Google Scholar
  9. 9.
    Depril, D., Van Mechelen, I., Mirkin, B.: Algorithms for additive clustering of rectangular data tables. Computational Statistics and Data Analysis 52(11), 4923–4938 (2008)CrossRefMATHMathSciNetGoogle Scholar
  10. 10.
    Wilderjans, T.F., Depril, D., Mechelen, I.V.: Additive biclustering: A comparison of one new and two existing als algorithms. J. of Classification 30(1), 56–74 (2012)CrossRefGoogle Scholar
  11. 11.
    Ben N’Cir, C.E., Cleuziou, G., Essoussi, N.: Identification of non-disjoint clusters with small and parameterizable overlaps. In: 2013 Int. Conf. on Computer Applications Technology (ICCAT), pp. 1–6 (2013)Google Scholar
  12. 12.
    Tsai, D.M., Lin, C.C.: Fuzzy c-means based clustering for linearly and nonlinearly separable data. Pattern Recogn. 44(8), 1750–1760 (2011)CrossRefMATHGoogle Scholar
  13. 13.
    Amigo, E., Gonzalo, J., Artiles, J., Verdejo, F.: A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information Retrieval 12(4), 461–486 (2009)CrossRefGoogle Scholar

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

Personalised recommendations