%0 Journal Article
%T HMM Based Symbolic Sequence Self Organizing Clustering<br>基于隐马尔可夫模型的符号序列自组织聚类
%A LU Yu
%A CHENG Dai-Jie
%A <br>吕昱
%A 程代杰
%J 计算机科学
%D 2006
%I 
%X In this paper, we propose a model-based, self organizing feature map algorithm for the clustering of variable-length sequences. Hidden Markov models(HMMs) are used as representations for the cluster centers, and batch map training algorithm is applied in clustering procedure. Simulation results show that our method can successfully find patterns of clusters of the input variable-length sequences.
%K Batch map
%K SOM
%K Hidden markov model
%K Symbolic sequence clustering<br>批处理自组织特征映射
%K 隐马尔可夫模型
%K 符号序列聚类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=7284E97F65ACE244&yid=37904DC365DD7266&vid=27746BCEEE58E9DC&iid=5D311CA918CA9A03&sid=AA27B676BFCAA4BE&eid=D2742EEE6F4DF8FE&journal_id=1002-137X&journal_name=计算机科学&referenced_num=2&reference_num=14