A Hidden Markov Model-Based K-Means Time Series Clustering Algorithm

摘要：

Aimed at some shortages in the existing time series clustering methods based on hidden Markov model(HMM),such as longer sequence and equal length, a hidden Markov model-based k-means time series clustering algorithm is proposed, whose objective function is the joint likelihood function. At first, an initial partition is obtained by unsupervised clustering of the time series using dynamic time warping (DTW), then HMMs are built from it, and the initial clusters serve as input to a process that trains one HMM on each cluster and iteratively moves time series between clusters based on their likelihoods given the various HMMs.

作者: Li-Li Wei Jing-Qiang Jiang

作者单位: School of Mathematics and Computer Science Ningxia University Yinchuan 750021,China

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 135-138

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）