Multi-grain hierarchical topic extraction algorithm for text mining
Expert Systems with Applications: An International Journal
A method for determination on HMM distance threshold
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
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
A review on time series data mining
Engineering Applications of Artificial Intelligence
Semantic multi-grain mixture topic model for text analysis
Expert Systems with Applications: An International Journal
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Model-based clustering is one of the most important ways for time series data mining. However, the process of clustering may encounter several problems. In this paper, a novel clustering algorithm of time-series which incorporates recursive Hidden Markov Model(HMM) training is proposed. Our contributions are as follows: 1) We recursively train models and use these model information in the process agglomerative hierarchical clustering. 2) We built HMM of timeseries clusters to describe clusters. To evaluate the effectiveness of the algorithm, several experiments are conducted on both synthetic data and real world data. The result shows that the proposed approach can achieve better performance in correctness rate than the traditional HMM-based clustering algorithm.