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Abstract

Local patterns in the form of single clusters are of interest in various areas of data mining. However, since the intention of cluster analysis is a global partition of a data set into clusters, it is not suitable to identify single clusters in a large data set where the majority of the data can not be assigned to meaningful clusters. This paper presents a new objective function-based approach to identify a single good cluster in a data set making use of techniques known from prototype-based, noise and fuzzy clustering. The proposed method can either be applied in order to identify single clusters or to carry out a standard cluster analysis by finding clusters step by step and determining the number of clusters automatically in this way.

Keywords

Cluster analysis local pattern discovery 

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References

  1. 1.
    Bezdek, J.C., Keller, J., Krishnapuram, R., Pal, N.R.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic Publishers, Boston (1999)zbMATHGoogle Scholar
  2. 2.
    Davé, R.N.: Characterization and Detection of Noise in Clustering. Pattern Recognition Letters 12, 657–664 (1991)CrossRefGoogle Scholar
  3. 3.
    Duczmal, L., Assunção, R.: A Simulated Annealing Strategy for the Detection of Arbitrarily Shaped Spatial Clusters. Computational Statistics & Data Analysis 45, 269–286 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Georgieva, O., Klawonn, F., Härtig, E.: Fuzzy Clustering of Macroarray Data. In: Reusch, B. (ed.) Computational Intelligence, Theory and Applications, pp. 83–94. Springer, Berlin (2005)CrossRefGoogle Scholar
  5. 5.
    Höppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy Cluster Analysis. Wiley, Chichester (1999)zbMATHGoogle Scholar
  6. 6.
    Klawonn, F., Georgieva, O.: Identifying Single Clusters in Large Data Sets. In: Wang, J. (ed.) Encyclopedia of Data Warehousing and Mining, pp. 582–585. Idea Group, Hershey (2006)Google Scholar
  7. 7.
    Kulldorff, M.: A Spatial Scan Statistic. Communications in Statistics 26, 1481–1496 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Zhang, C., Zhang, S.: Association Rule Mining. Springer, Berlin (2002)zbMATHCrossRefGoogle Scholar
  9. 9.
    Zhang, Z., Hand, D.J.: Detecting Groups of Anomalously Similar Objects in Large Data Sets. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds.) IDA 2005. LNCS, vol. 3646, pp. 509–519. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Frank Klawonn
    • 1
  1. 1.Department of Computer ScienceUniversity of Applied Sciences Braunschweig/WolfenbuettelWolfenbuettelGermany

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