Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora

Mining cluster evolution from multiple correlated time-varying text corpora is important in exploratory text analytics. In this paper, we propose an approach called evolutionary hierarchical Dirichlet processes (EvoHDP) to discover interesting cluster evolution patterns from such text data. We formulate the EvoHDP as a series of hierarchical Dirichlet processes (HDP) by adding time dependencies to the adjacent epochs, and propose a cascaded Gibbs sampling scheme to infer the model. This approach can discover different evolving patterns of clusters, including emergence, disappearance, evolution within a corpus and across different corpora. Experiments over synthetic and real-world multiple correlated timevarying data sets illustrate the effectiveness of EvoHDP on discovering cluster evolution patterns. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning; I.5.3 [Pattern Recognition]: Clustering; G.3 [Probability and Statistics]: Nonparametric statistics; H.2...

• Type: Conference
• Year: 2010
• Where: KDD
• Authors: Jianwen Zhang, Yangqiu Song, Changshui Zhang, Shixia Liu
• Keywords: Cluster Evolution, Data Mining, Dirichlet Processes, Hierarchical Dirichlet Processes, KDD 2010