A Second-order Hidden Markov Model-based Agglomerative Hierarchical Time-series Clustering Algorithm
SU Jinqi
Abstract：In this paper ,a second-order hidden markov model(HMM2) is proposed to overcome disadvantage of tradi-tional HMM for premise .A second-order hidden markov model-based agglomerative hierarchical time-series clustering algo-rithm is put forward on the foundation of stydying HMM 2 and agglomerative hierarchical algorithm in the use of time-spatial analysis .In this algorithm ,HMM are built from time-series ,the relationship between the probality and model's historical state is considered reasonably ,and the series are clustered according to the most similarity ,then represent by models ,and then the process of traning and merging and updating initial models is iterated until the final result is obtained .In the experi-ment ,the clustering quality of this algorithm and the algorithm based on HMM ,the relation between correctness rate and the clustering number ,the relation between correctness rate and the model distance are researched .The results show that the algorithm in this paper can achieve better performance than the traditional HMM-based clustering algorithm .
Keywords：second-order hidden markov modelaggregate clusteringsimilarity measureseries analysis
Publication Date：2014-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 1119-1122,1126 )