DOI QR코드

DOI QR Code

A Density-based Clustering Method

  • Published : 2002.12.01

Abstract

This paper is to show a clustering application of a density estimation method that utilizes the Gaussian mixture model. We define "closeness measure" as a clustering criterion to see how close given two Gaussian components are. Closeness measure is defined as the ratio of log likelihood between two Gaussian components. According to simulations using artificial data, the clustering algorithm turned out to be very powerful in that it can correctly determine clusters in complex situations, and very flexible in that it can produce different sizes of clusters based on different threshold valuesold values

Keywords

References

  1. The Korean Communications in Statistics v.8 no.1 A Penalized Likelihood Method for Model Complexity Reduction in Gaussian Mixture Density Ahn, S.M.
  2. Bioinformatics (Oxford, England) v.16 On the Convergence of a Clustering Algorithm foe Protein-coding Regions in Microbial Genomes Baldi, P. https://doi.org/10.1093/bioinformatics/16.4.367
  3. IEEE trans. On Informationa Theory v.37 no.4 Minimum Complexity Density Estimation Barron, A. R.;Cover, T. M. https://doi.org/10.1109/18.86996
  4. Journal of Royal Statistical Society(B) v.37 Maximum Likelihood from Incomplete Data via the EM Algorithm Dempster, A. P.;Laird, N. M.;Rubin, D. B.
  5. Bioinformatics (Oxford, England) v.18 A mixture model-base approach to the clustering of microarray expression data McLachlan, G. J.;Bean, R. W.;Peel, D. https://doi.org/10.1093/bioinformatics/18.3.413
  6. Bioinformatics (Oxford, England) v.18 Bayesian infinite mixture model baed clustering of gene expression profiles Medvedovic, M.;Sivaganesan, S. https://doi.org/10.1093/bioinformatics/18.9.1194
  7. Proceedings of International Conference on Image Processing v.1 Motion-based video segmentation using fuzzy clustering ad classical mixture model Nitsuwat, S.;Jin, J.S.;Hudson, H.M.
  8. Proceedings of 2001 International Conference on Image Processing v.2 Image segmentation based on statistically principled clustering Paulwels, E.J.;Frederix, G.;Caenen, G.
  9. Journal of The Royal Statistical Society, Series B v.49 no.3 Stochastic Complexity Rissanen, J.
  10. Proceedings of 11th International Conference on Image Analysis and Processing Spatial clustering of pixels in the mouth area of face images Sadeghi, M.;Kittler, J.;Messer, K.
  11. The Annals of Statistics v.6 no.2 Estimating the Dimension of a Model Schwarz, G. https://doi.org/10.1214/aos/1176344136
  12. Magnetic Resonance Imaging v.17 A modified fuzzy clustering algorithm for opreator independent brain tissue classification of dual echo MR images Suckling, J.;Sigmundsson, T.;Greenwood, K.;Bullmore, E T https://doi.org/10.1016/S0730-725X(99)00055-7
  13. Journal of Computational and Graphical Statistics v.4 A Visualization Techniqeu for Studying the Iterative Estimation of Mixture Densities Solka, J. L.;Poston, W. L.;Wegman, E. J. https://doi.org/10.2307/1390846
  14. Statistical Analysis of Finite Mixture Distributions Titterington, D. M.;Smith, A. F.;Makov, U. E.
  15. Bioinformatics (Oxford, England) v.17 Model-based clustering and data transformations for gene expression data Yeung, K. Y.;Fraley, C.;Murua, A.;Raftery, A. E.;Ruzzo, W. L. https://doi.org/10.1093/bioinformatics/17.10.977