Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Model-Based Hierarchical Clustering
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A Model Selection Criterion for Classification: Application to HMM Topology Optimization
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
The Journal of Machine Learning Research
Simple Semantics in Topic Detection and Tracking
Information Retrieval
Probabilistic model-based clustering of complex data
Probabilistic model-based clustering of complex data
Tracking dynamics of topic trends using a finite mixture model
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Generative model-based document clustering: a comparative study
Knowledge and Information Systems
A mixture model for contextual text mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A General Framework for Agglomerative Hierarchical Clustering Algorithms
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Incremental hierarchical clustering of text documents
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A new distance measure for hidden Markov models
Expert Systems with Applications: An International Journal
Multi-grain hierarchical topic extraction algorithm for text mining
Expert Systems with Applications: An International Journal
Hierarchical clustering for topic analysis based on variable feature selection
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Temporal expert finding through generalized time topic modeling
Knowledge-Based Systems
Semantic multi-grain mixture topic model for text analysis
Expert Systems with Applications: An International Journal
Topics modeling based on selective Zipf distribution
Expert Systems with Applications: An International Journal
Web objectionable text content detection using topic modeling technique
Expert Systems with Applications: An International Journal
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Discovering topics from large amount of documents has become an important task recently. Most of the topic models treat document as a word sequence, whether in discrete character or term frequency form. However, the number of words in a document is greatly different from that in other documents. This will lead to several problems for current topic models in dealing with topics analysis. On the other hand, it is difficult to perform topic transition analysis based on current topic models. In an attempt to overcome these deficiencies, a variable space hidden Markov model (VSHMM) is proposed to represent the topics, and several operations based on space computation are presented. A hierarchical clustering algorithm with dynamically changing of the component number in topic model is proposed to demonstrate the effectiveness of the VSHMM. Method of document partition based on topic transition is also present. Experiments on a real-world dataset show that the VSHMM can improve the accuracy while decreasing the algorithm's time complexity greatly compared with the algorithm based on current mixture model.