为弥补传统的基于隐M arkov模型在前提假设上的不足，提出了二阶隐马尔可夫模型。在研究二阶隐马尔可夫模型和凝聚算法在时空序列分析的基础上，提出了一种新的基于 HMM2的时间序列凝聚算法。该算法应用 HMM2对时间序列进行建模，合理考虑了概率和模型历史状态的关联性，按照相异度原则将序列聚成几个类，每个类用模型代表，进而对这些模型训练、合并及迭代得到聚类结果。实验比较了该算法与基于HMM算法的聚类质量，研究了聚类正确率与聚类数、距离正确率与模型距离的关系。结果表明，该算法比传统的基于HMM的聚类算法具有更好的性能。%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 .
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