Anna Szymkowiak Have, Mark A. Girolami, Jan Larsen
Abstract: Methods for spectral clustering have been proposed
recently which rely on the eigenvalue decomposition of an affinity
matrix. In this work it is proposed that the affinity matrix
is created based on the elements of a non-parametric density
estimator. This matrix is then decomposed to obtain posterior
probabilities of class membership using an appropriate form of
nonnegative matrix factorization. The troublesome selection of
hyperparameters such as kernel width and number of clusters
can be obtained using standard cross-validation methods as is
demonstrated on a number of diverse data sets.
Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results
Ian Davidson1 and S.S. Ravi1
(1) Department of Computer Science, University at Albany - State University of New York, Albany, NY 12222,
Handcock, M.S., Raftery, A.E. and Tantrum, J. (2005).
Model-Based Clustering for Social Networks.
Working Paper no. 46, Center for Statistics and the Social Sciences,
University of Washington.
C. Brooks, and N. Montanez. WWW '06: Proceedings of the 15th international conference on World Wide Web, page 625-632. New York, NY, USA, ACM Press, (2006)