WMCA: A Weighted Matrix Coverage Based Approach to Cluster Multivariate Time Series
Zhuo Fei-bao, Tianqiang Huang, Gongde Guo · 2009
The variables of multivariate time series (MTS) can be numeric or categorical attribute, but many researches payed attention to numeric attribute. This paper focuses on MTS with mixed attributes. A novel approach of weighted matrix coverage is proposed to judge the neighborhood between MTS based on Singular Value Decomposition (SVD) and a notion about the number of common neighbors (NCN) is introduced to measure the similarities. In turn, a modified hierarchical clustering algorithm is put forward. The experimental results show that our algorithm performs better than the standard hierarchical clustering algorithm based on Dynamic Time Wrapping (DTW) distance metric.