Text Document Latent Subspace Clustering by PLSA Factors

Text documents are often high dimensional and sparse, it is a great challenge to discover the clusters among the unlabelled text data, because there are no obvious clusters by common distance measure. In this paper we present a latent subspace clustering method to find text clusters. In our algorithm, we use latent factors extracted by probability latent semantic analysis (PLSA) to generate latent clustering subspaces, and then use the distance between sample and each latent clustering subspace as similarity for text clustering. On some text document datasets our method shows effective implementation for text clustering.