Maximum within-cluster association
SCIE
SCOPUS
- Title
- Maximum within-cluster association
- Authors
- Lee, YJ; Choi, SJ
- Date Issued
- 2005-07-15
- Publisher
- ELSEVIER SCIENCE BV
- Abstract
- This paper addresses a new method and aspect of information-theoretic Clustering where we exploit the minimum entropy principle and the quadratic distance measure between probability densities, We present a new minimum entropy objective function which leads to the maximization or within-cluster association, A simple implementation using the gradient ascent method is given. In addition, we show that the Minimum entropy principle leads to the objective function of the k-means clustering, and the maximum within-cluster association is closed related to the spectral clustering which is an eigen-decomposition-based method. This information-theoretic view of spectral clustering leads us to use the kernel density estimation method in constructing an affinity matrix. (c) 2004 Elsevier B.V. All rights reserved.
- Keywords
- clustering; information-theoretic learning; minimum entropy; spectral clustering
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/24530
- DOI
- 10.1016/j.patrec.2004.11.025
- ISSN
- 0167-8655
- Article Type
- Article
- Citation
- PATTERN RECOGNITION LETTERS, vol. 26, no. 10, page. 1412 - 1422, 2005-07-15
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