Hierarchical Clustering for Datamining

Abstract

. This paper presents hierarchical probabilistic clustering methods for unsupervised and supervised learning in datamining applications. The probabilistic clustering is based on the previously suggested Generalizable Gaussian Mixture model. A soft version of the Generalizable Gaussian Mixture model is also discussed. The proposed hierarchical scheme is agglomerative and based on a L 2 distance metric. Unsupervised and supervised schemes are successfully tested on artificially data and for segmention of e-mails. 1