Building and Assessing a Constrained Clustering Hierarchical Algorithm

  • Eduardo R. Concepción Morales
  • Yosu Yurramendi Mendizabal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)

Abstract

Unsupervised classification or clustering has been used in many disciplines and contexts. Traditional methodologies are mostly based on the minimization of the distance between data and the cluster means without considering any other possible relationship present in data, e.g., spatial interactions. A constrained hierarchical agglomerative algorithm with an aggregation index is introduced which uses neighbouring relations present in the data. Experiments show the behaviour of the proposed constrained algorithm in different situations.

Keywords

constrained clustering methods agglomerative hierarchical classification 

References

  1. 1.
    Jain, A.K., Murthy, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31, 264–323 (1999)CrossRefGoogle Scholar
  2. 2.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2000)MATHGoogle Scholar
  3. 3.
    Puzicha, J., Hofmann, T., Buhmann, J.: A Theory of Proximity Based Clustering: Structure Detection by Optimization. Pattern Recognition 33, 617–634 (1999)CrossRefGoogle Scholar
  4. 4.
    Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. IEEE Trans. Patt. Anal. Mach. Intelligence 22, 888–905 (2000)CrossRefGoogle Scholar
  5. 5.
    Tobler, W.R.: Cellular Geography. In: Gale, S., Olson, G. (eds.) Philosophy in Geography, pp. 379–386. Reidel, Dordrecht (1979)CrossRefGoogle Scholar
  6. 6.
    Winkler, G.: Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. Springer, Berlin (2003)CrossRefMATHGoogle Scholar
  7. 7.
    Roweis, S., Saul, L.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)CrossRefGoogle Scholar
  8. 8.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Englewoods Cliffs (1988)MATHGoogle Scholar
  9. 9.
    Forgy, E.: Cluster Analysis of Multivariate Data: Efficiency Versus Interpretability of Classification. Biometrics 21, 768–780 (1965)Google Scholar
  10. 10.
    MacQueen, J.: Some Methods of Classification and Analysis of Multivariate Observations. In: Cam, L.M.L., Neyman, J. (eds.) Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, Berkeley (1967)Google Scholar
  11. 11.
    Concepción, E., Yurramendi, Y.: Using Spatial Autocorrelation Measures for Data Clustering. In: 12th Conference of the Spanish Association for Artificial Intelligence, CAEPIA- TTIA, Salamanca (2007)Google Scholar
  12. 12.
    Cliff, A.D., Ord, K.J.: Spatial processes. Models and applications. Pion, London (1981)MATHGoogle Scholar
  13. 13.
    Legendre, P., Legendre, L.: Numerical ecology. Elsevier, Amsterdam (1998)MATHGoogle Scholar
  14. 14.
    Lebart, L.: Analyse Statistique de la Contiguïté. Publication de l’Institut de Statistiques de l’Université de Paris 28, 81–112 (1969)MATHGoogle Scholar
  15. 15.
    Lebart, L.: Contiguity Analysis and Classification. In: Gaul, W., Opitz, O., Schader, M. (eds.) Data Analysis, pp. 233–244. Springer, Berlin (2000)CrossRefGoogle Scholar
  16. 16.
    Jambu, M., Lebeaux, M.-O.: Cluster Analysis and Data Analysis. North-Holland, Amsterdam (1983)MATHGoogle Scholar
  17. 17.
    Lance, G.N., Williams, W.T.: A General Theory of Classificatory Sorting Strategies. I. Hierarchical Systems. Computer Journal 9, 373–380 (1967)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Eduardo R. Concepción Morales
    • 1
  • Yosu Yurramendi Mendizabal
    • 2
  1. 1.Faculty of InformaticsUniversity of CienfuegosCuatro CaminosCuba
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of the Basque Country/EHUDonostia-San SebastianSpain

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