Advertisement

Clustering

  • Martin Ester
  • Jörg Sander

Zusammenfassung

In diesem Kapitel wird ein Überblick über grundlegende Methoden und Techniken zur Clusteranalyse gegeben. Es werden verschiedene Algorithmen für unterschiedliche Anwendungsbereiche vorgestellt. Besonderer Wert wird auch auf die Diskussion der Probleme der einzelnen Verfahren und die Darstellung von neueren Techniken zu ihrer Leistungssteigerung gelegt.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. Agrawal R., Gehrke J., Gunopulos D., Raghavan R. 1998, „Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications“, Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD’98). ACM Press, New York, NY, pp. 94–105.Google Scholar
  2. Ankerst M., Breunig M., Kriegel H.-P., Sander J. 1999, „OPTICS: Ordering Points To Identify the Clustering Structure“, Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD’99). ACM Press, New York, NY, pp. 49–60.Google Scholar
  3. Bozdogan H. 1983, „Determining the Number of Component Clusters in the standard multivariate normal mixture model using model-selection criteria“, Technical Report UIC/DQM/A83–1. Quantitative Methods Dept., University of Illinois, Chicago.Google Scholar
  4. Dempster A. P., Laird N. M., Rubin D. B. 1977, „Maximum Likelyhood from Incomplete Data via the EM algorithm“, Journal of the Royal Statistical Society; Series B, 39(1), pp. 1–31.MathSciNetzbMATHGoogle Scholar
  5. Ester M., Kriegel H.-P., Sander J., Xu X. 1996, „A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise“, Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD’96). AAAI Press, Menlo Park, CA, pp. 226–231.Google Scholar
  6. Ester M., Kriegel H.-P, Sander J., Xu X. 1998, „Clustering for Mining in Large Spatial Databases“, KI Künstliche Intelligenz (Themenheft Data Mining) 1 (1998). ScienTec Publishing, Bad Ems, pp. 18–24.Google Scholar
  7. Ester M., Kriegel H.-P., Sander J., Wimmer M., Xu X. 1998, „Incremental Clustering for Mining in a Data Warehousing Environment“, Proc. 24th Int. Conf. on Very Large Databases (VLDB’98) Morgan Kaufmann Publishers, San Francisco, California, pp. 323–333.Google Scholar
  8. Ester M., Kriegel H.-R, Xu X. 1995a, „Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Identification, Lecture Notes in Computer Science (Proc. 4th Int. Symposium on Large Spatial Databases (SSD’95)), Vol. 591, Springer, 1995, pp. 67–82.Google Scholar
  9. Ester M., Kriegel H.-P., Xu X. 1995b, „A Database Interface for Clustering in Large Spatial Databases“, Proc. 1st Int. Conf. on Knowledge Discovery and Data Mining (KDD’95), AAAI Press, Menlo Park, CA, pp. 94–99.Google Scholar
  10. Fayyad U., Reina C., Bradley P. S. 1998, „Initialization of Iterative Refinement Clustering Algorithms“, Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining (KDD’98), AAAI Press, Menlo Park, CA, pp. 194–198.Google Scholar
  11. Forgy E. W. 1965, „Cluster analysis of multivariate data: Efficiency vs. interpretability of classification (abstract)“, Biometrics, Vol. 21, pp. 768–769.Google Scholar
  12. Huang Z. 1997, „A Fast Algorithm to Cluster Very Large Categorical Data Sets in Data Mining“, Proc. SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Technical Report 97–07, University of British Columbia, Vancouver, Canada.Google Scholar
  13. Jain A. K., Dubes R. C., 1988, Algorithms for Clustering Data,Prentice-Hall.zbMATHGoogle Scholar
  14. Kaufman L., Rousseeuw P. J. 1990, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons.CrossRefGoogle Scholar
  15. MacQueen, J. 1967, „Some Methods for Classification and Analysis of Multivariate Observations“, 5th Berkeley Symp. Math. Statist. Prob., Volume 1, pp. 281–297.MathSciNetGoogle Scholar
  16. Murtagh F. 1983, „A Survey of Recent Advances in Hierarchical Clustering Algorithms“, The Computer Journal, Vol 26. No. 4, pp. 354–359.zbMATHCrossRefGoogle Scholar
  17. Ng R. T., Han J., 1994, „Efficient and Effective Clustering Methods for Spatial Data Mining“, Proc. 20th Int. Conf. on Very Large Data Bases (VLDB’94), Morgan Kaufmann Publishers, San Francisco, California, pp. 144–155.Google Scholar
  18. Nievergelt J., Hinterberger H., Sevcik K. C. 1984, „The Grid file: An Adaptable, Symmetric Multikey File Structure“, ACM Trans. Database Systems Vol. 9, No. 1, pp. 38–71.CrossRefGoogle Scholar
  19. Rohlf F. J. 1973, „Hierarchical clustering using the minimum spanning tree“, The Computer Journal, Vol16, No. 1, pp. 93–95.Google Scholar
  20. Schikuta E. 1996, „Grid clustering: An efficient hierarchical clustering method for very large data sets“, Proc. 13th Int. Conf. on Pattern Recognition, Vol. 2, IEEE Computer Society Press, Los Alamitos, California, pp. 101–105.Google Scholar
  21. Sander J., Ester M., Kriegel H.-P., Xu X. 1998, „Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications“, Data Mining and Knowledge Discovery, An International Journal, Kluwer Academic Publishers, Norwell, MA, Vol. 2, No. 2., 1998, pp. 169–194.Google Scholar
  22. Stonebraker M., Frew J., Gardels K., and Meredith J. 1993, „The SEQUOIA 2000 Storage Benchmark“, Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD’93). ACM Press, New York, pp. 2–11.Google Scholar
  23. Zhang T., Ramakrishnan R., Linvy M. 1996, „BIRCH: An Efficient Data Clustering Method for Very Large Databases“, Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD’96), ACM Press, New York, pp. 103–114.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Martin Ester
    • 1
  • Jörg Sander
    • 1
  1. 1.Institut für InformatikLudwig-Maximilians-UniversitätMünchenDeutschland

Personalised recommendations