Paper Info
Title
Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora
Abstract
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 time-varying data sets illustrate the effectiveness of EvoHDP on discovering cluster evolution patterns.
Year
DOI
Venue
2010
10.1145/1835804.1835940
KDD
Keywords
Field
DocType
different corpus,interesting cluster evolution pattern,exploratory text analytics,multiple correlated time-varying text,multiple correlated time-varying data,multiple correlated time-varying corpus,evolutionary hierarchical dirichlet,hierarchical dirichlet,text data,evolutionary hierarchical dirichlet process,cluster evolution pattern,mining cluster evolution,gibbs sampling,clustering,hierarchical dirichlet process,bayesian,mixture models
Data mining,Hierarchical Dirichlet process,Cluster (physics),Latent Dirichlet allocation,Computer science,Text corpus,Artificial intelligence,Dirichlet distribution,Cluster analysis,Machine learning,Mixture model,Gibbs sampling
Conference
Citations 
PageRank 
References 
61
2.12
22
Authors
4
Name
Order
Citations
PageRank
Jianwen Zhang131914.74
Yangqiu Song22045103.29
Changshui Zhang35506323.40
Shixia Liu4209582.41