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This paper presents our clustering technique based on generalized SOMs with evolving splittingmerging tree-like structures and its application to complex clustering problems including some benchmark data sets and, first of all, WWW-document clustering. Our approach that works in a fully unsupervised way (i.e., without the pre-defined cluster number and using unlabelled data), automatically detects the number of clusters and generates multiprototypes for them. The collection of 548 abstracts of technical reports as well as its 476-element subset, both available at WWW server of the Department of Computer Science, University of Rochester, USA (www.cs.rochester.edu/trs) are the subjects of clustering. A comparative analysis with five alternative clustering techniques is also carried out. The reported results prove that our approach is a powerful tool (that outperforms several alternative approaches) for complex cluster-analysis tasks including the problems of WWW-document clustering.

Generalized SOMs with Splitting-Merging Tree-Like Structures for WWW-Document Clustering