TY - GEN
T1 - A nonparametric information theoretic clustering algorithm
AU - Faivishevsky, Lev
AU - Goldberger, Jacob
PY - 2010
Y1 - 2010
N2 - In this paper we propose a novel clustering algorithm based on maximizing the mutual information between data points and clusters. Unlike previous methods, we neither assume the data axe given in terms of distributions nor impose any parametric model on the within-cluster distribution. Instead, we utilize a non-parametric estimation of the average cluster entropies and search for a clustering that maximizes the estimated mutual information between data points and clusters. The improved performance of the proposed algorithm is demonstrated on several standard datasets.
AB - In this paper we propose a novel clustering algorithm based on maximizing the mutual information between data points and clusters. Unlike previous methods, we neither assume the data axe given in terms of distributions nor impose any parametric model on the within-cluster distribution. Instead, we utilize a non-parametric estimation of the average cluster entropies and search for a clustering that maximizes the estimated mutual information between data points and clusters. The improved performance of the proposed algorithm is demonstrated on several standard datasets.
UR - http://www.scopus.com/inward/record.url?scp=77956520100&partnerID=8YFLogxK
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AN - SCOPUS:77956520100
SN - 9781605589077
T3 - ICML 2010 - Proceedings, 27th International Conference on Machine Learning
SP - 351
EP - 358
BT - ICML 2010 - Proceedings, 27th International Conference on Machine Learning
T2 - 27th International Conference on Machine Learning, ICML 2010
Y2 - 21 June 2010 through 25 June 2010
ER -