Abstract: Hidden Markov Model has been widely used as a valuable stochastic model. A novel clustering method based on both self-splitting and merging competitive learning and HMMs is proposed to improve the quality of the clustering. Experimental results show that our algorithm is very effective for such gene expression datasets.

摘要： 隐马尔可夫模型(Hidden Markov Model) 是一种双随机过程,被广泛地应用于信号处理和模式识别中。但当将其应用于聚类时, 对隐马尔可夫模型的训练，即参数估计，是一个非常重要的问题,训练方法的优劣将对整个应用效果产生重要的影响。本文针对这一问题，将自劈分合并竞争学习运用于HMMs。实验结果证实了本文提出方法的有效性。