A Novel Hidden Markov Model-Based Hierarchical Time-Series Clustering Algorithm

Duan Jiangjiao; Xue Yongsheng; Lin Ziyu; Wang Wei; Shi Baile

Journal of Computer Research and Development > 2006 > 43(1): 61-67.

Duan Jiangjiao, Xue Yongsheng, Lin Ziyu, Wang Wei, Shi Baile. A Novel Hidden Markov Model-Based Hierarchical Time-Series Clustering Algorithm[J]. Journal of Computer Research and Development, 2006, 43(1): 61-67.

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A Novel Hidden Markov Model-Based Hierarchical Time-Series Clustering Algorithm

1(Department of Computer Science, Xiamen University, Xiamen 361005) 2(Department of Computing and Information, Fudan University, Shanghai 200433) 3(Department of Computer Science and Technology, Peking University, Beijing 100871)

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Published Date: January 14, 2006

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Abstract

Abstract

In this paper, a novel hidden Markov model (HMM)-based hierarchical time-series clustering algorithm HBHCTS is proposed, because of the disadvantage of traditional HMM-based clustering algorithms for time-series. The main purpose is to improve clustering quality and represent the clusters easily at the same time. In HBHCTS, HMMs are built from time-series, and the initial models are obtained according to the most similarity, and then the process of merging and updating initial models is iterated until the final result is obtained. In the experiment, the relation between correctness rate and the length of a sequence, the relation between correctness rate and the model distance are researched. The results show that the HBHCTS can achieve better performance in correctness rate than the traditional HMM-based clustering algorithm.
- model-based,
- clustering,
- HMM,
- time-series

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