Research on Analysis Method of Multivariate Time Series Clustering Based on Principal Component
Guo Xiao-fang · Measurement & Control Technology · 2012
To improve the efficiency of algorithms for multivariate time series(MTS) data clustering,a method of MTS clustering based on principal component analysis is proposed.A series of unrelated clusters are obtained by linear combination.According to extended Euclid norm the element and the remaining elements of the cluster between principal component,clustering analysis of MTS is done.The theoretical analysis and experimental results show that the clustering quality and operation time of proposed algorithm are better than that of direct K-means clustering method obviously.