TITLE: Hierarchical Clustering for Datamining
AUTHORS: Anna Szymkowiak, Jan Larsen, Lars Kai Hansen
Informatics and Mathematical Modelling, Building 321
Technical University of Denmark, DK-2800 Lyngby, Denmark
emails: asz,jl,lkhansen@imm.dtu.dk
www: http://eivind.imm.dtu.dk
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 ${\cal L}_2$
distance metric. Unsupervised
and supervised schemes are successfully tested on artificially
data and for segmention of e-mails.
Apperas in special session on neural networks and datamining at KES-2001 Fifth International Conference on Knowledge-Based Intelligent Information Engineering Systems & Allied Technologies , Osaka and Nara, Japan, September 6-8, 2001.