Probabilistic Models for Hierarchical Clustering and Categorisation : Applications in the information Society
Eric Gaussier, Cyril Goutte
We propose a new hierarchical generative model for textual data, where words may be generative model for
textual data, where words may be generated by topic specific distributions at any level in the hierarchy.
This model is naturally well-suited to clustering documents in preset or automatically generated hierarchies
, as well as categorising new documents in an existing hierarchy. Furthermore, we present a series of
applications that can benefit from our model, as well as an experimental evaluation for both clustering and
categorisation on frequently used data sets.
Proceedings of the Intl. Conf. on Advances in Infrastructure for Electronic Business, Education, Science and Medicine on the
Internet, L'Aquila,Italy, January 21-27 2002.
gaussier02probabilistic.ps.gz (50.64 kB)