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Shape-Invariant Cluster Validity Indices

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Book cover Advances in Data Mining (ICDM 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3275))

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Abstract

This paper discusses two cluster validity indices that quantify the quality of a putative clustering in terms of label-homogeneity and connectivity. Because the indices are defined in terms of local data-density, they do not favour spherical or ellipsoidal clusters as other validity indices tend to do. A statistics-based decision framework is outlined that uses these indices to decide on the correct number of clusters.

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References

  1. Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, N.R.: Fuzzy Models and Algorithms for Pattern Recognition in Image Processing. Kluwer Academic Publishers, Dordrecht (1999)

    Book  MATH  Google Scholar 

  2. Dunn, J.C.: Well Separated Cluserds and Optimal Fuzzy Partitions. Journal of Cybernetics 4, 95–104 (1974)

    Article  MATH  Google Scholar 

  3. Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31(3), 264–323 (1999)

    Article  Google Scholar 

  4. Jajuga, K., Sokolowski, A., Bock, H.: Classification, Clustering and Data Analysis. In: IFCS Conference, Poland. Springer, Heidelberg (2002)

    Google Scholar 

  5. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. J. Wiley and Sons, Chichester (1990)

    Book  MATH  Google Scholar 

  6. Pauwels, E.J., Frederix, G.: Finding Salient Regions in Images. Computer Vision and Image Understanding 75(1/2), 73–85 (1999)

    Article  Google Scholar 

  7. Xie, X.L., Beni, G.: A Validity Measure for Fuzzy Clustering. IEEE Trans on Pattern Analysis and Machine Intelligence 13(8), 841–847 (1991)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Dept IWT, Hogeschool Limburg, Universitaire Campus, Gebouw H, B-3590, Diepenbeek, Belgium

    Greet Frederix

  2. Centre for Mathematics and Computer Science, CWI Kruislaan 413, 1098SJ, Amsterdam, The Netherlands

    Eric J. Pauwels

Authors
  1. Greet Frederix
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  2. Eric J. Pauwels
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and applied Computer Sciences, IBaI, Germany

    Petra Perner

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© 2004 Springer-Verlag Berlin Heidelberg

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Frederix, G., Pauwels, E.J. (2004). Shape-Invariant Cluster Validity Indices. In: Perner, P. (eds) Advances in Data Mining. ICDM 2004. Lecture Notes in Computer Science(), vol 3275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30185-1_11

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  • DOI: https://doi.org/10.1007/978-3-540-30185-1_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24054-9

  • Online ISBN: 978-3-540-30185-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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