Model-based document clustering with a collapsed gibbs sampler
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Latent Dirichlet Bayesian Co-Clustering
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
A Generic Approach to Topic Models
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
A statistical model for topic segmentation and clustering
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Information retrieval with a simplified conceptual graph-like representation
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Co-clustering with augmented data matrix
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Typology of mixed-membership models: towards a design method
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Feature enriched nonparametric bayesian co-clustering
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
An unsupervised topic segmentation model incorporating word order
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
πLDA: document clustering with selective structural constraints
Proceedings of the 21st ACM international conference on Multimedia
Co-clustering with augmented matrix
Applied Intelligence
New fuzzy bi-clustering technique applied to the voltage stabilization of an electrical network
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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We present a generative model for simultaneously clustering documents and terms. Our model is a four-level hierarchical Bayesian model, in which each document is modeled as a random mixture of document topics , where each topic is a distribution over some segments of the text. Each of these segments in the document can be modeled as a mixture of word topics where each topic is a distribution over words. We present efficient approximate inference techniques based on Markov Chain Monte Carlo method and a Moment-Matching algorithm for empirical Bayes parameter estimation. We report results in document modeling, document and term clustering, comparing to other topic models, Clustering and Co-Clustering algorithms including Latent Dirichlet Allocation (LDA), Model-based Overlapping Clustering (MOC), Model-based Overlapping Co-Clustering (MOCC) and Information-Theoretic Co-Clustering (ITCC).