Knowledge discovery of multiple-topic document using parametric mixture model with dirichlet prior

Documents, such as those seen on Wikipedia and Folksonomy, have tended to be assigned with multiple topics as a meta-data. Therefore, it is more and more important to analyze a relationship between a document and topics assigned to the document. In this paper, we proposed a novel probabilistic generative model of documents with multiple topics as a meta-data. By focusing on modeling the generation process of a document with multiple topics, we can extract specific properties of documents with multiple topics. Proposed model is an expansion of an existing probabilistic generative model: Parametric Mixture Model (PMM). PMM models documents with multiple topics by mixing model parameters of each single topic. Since ,however, PMM assigns the same mixture ratio to each single topic, PMM cannot take into account the bias of each topic within a document. To deal with this problem, we propose a model that considers Dirichlet distribution as a prior distribution of the mixture ratio. We adopt ...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Issei Sato, Hiroshi Nakagawa
• Keywords: Data Mining, KDD 2007, Multiple Topics, PMM Models Documents, Probabilistic Generative Model