DOI QR코드

DOI QR Code

Estimating the Number of Clusters using Hotelling's

  • Choi, Kyung-Mee (Department of Mathematics, College of Science and Technology, Hongik University at Jochiwon)
  • Published : 2005.08.01

Abstract

In the cluster analysis, Hotelling's $T^2$ can be used to estimate the unknown number of clusters based on the idea of multiple comparison procedure. Especially, its threshold is obtained according to the probability of committing the type one error. Examples are used to compare Hotelling's $T^2$ with other classical location test statistics such as Sum-of-Squared Error and Wilks' $\Lambda$ The hierarchical clustering is used to reveal the underlying structure of the data. Also related criteria are reviewed in view of both the between variance and the within variance.

Keywords

References

  1. Duda, R.D., Hart, P. E., Stork, D.G. (2001). Pattern Classification. John Wiley Sons, Inc. New York
  2. Gallegos, M. T. (2002). Maximum likelihood clustering with outliers, Classification, Clustering, and Data Analysis(Jajuga et al Ed.), Springer
  3. Hastie, T., Tibshirani,R., Friedman, J. (2001). The Elements of Statistical Learning, Data Mining, Irference, and Prediction. Springer
  4. Jajuga.K., Sokolowski A., Bock H.-H. (Eds.) (2002). Classification, Clustering, and Data Analysis. Springer
  5. Kim,D. H. and Chung, C. W. (2003). Qcluster Relevance Feedback Using Adaptive Clustering for Content-Based Image Retrieval Proceedings of the ACM SIGMOD Conference
  6. Mardia, K.V., Kent, J.T., and Bibby, J.M. (1979). Multivariate Analysis. Academic Press
  7. Mojena, R. (1975). Hierarchical grouping methods and stopping rules: An evaluation. The Computer Journal, vol. 20, no. 4
  8. Rencher, A.C. (2002). Methods of Multivariate Analysis. John Wiley and Sons
  9. Rousseeuw, P. J. and Van Driessen, K. (1999). A first algorithm for the minimum covariance determinant estimator, Technometrics, vol. 41, 212-223 https://doi.org/10.2307/1270566
  10. Ward, J. H. (1963). Hierarchical Grouping to optimize an objective function. Journal of American Statistical Association, vol. 58, 236-244 https://doi.org/10.2307/2282967