Text clustering on latent thematic spaces: Variants, strengths and weaknesses

Deriving a thematically meaningful partition of an unlabeled text corpus is a challenging task. In comparison to classic term-based document indexing, the use of document representations based on latent thematic generative models can lead to improved clustering. However, determining a priori the optimal indexing technique is not straightforward, as it depends on the clustering problem faced and the partitioning strategy adopted. So as to overcome this indeterminacy, we propose deriving a consensus labeling upon the results of clustering processes executed on several document representations. Experiments conducted on subsets of two standard text corpora evaluate distinct clustering strategies based on latent thematic spaces and highlight the usefulness of consensus clustering to overcome the optimal document indexing indeterminacy.