An integrated probabilistic text clustering model with segment-based and word order evidence

Text clustering is an important research topic with many practical applications. Traditional clustering algorithms such as K-means and Probabilistic Latent Semantic Indexing (pLSI) simply treat each document as a single chunk of text and also ignore important word order information, which limits their performance. This paper proposes an integrated probabilistic model to explicitly combine the evidence from individual segments within a document and the word order information. Based on this model, a text clustering framework is proposed. Experiments on test datasets indicate substantial performance gains over state-of-the-art algorithms.