Clustering Unsynchronized Time Series Subsequences with Phase Shift Weighted Spherical k-means Algorithm

Time series have become an important class of temporal data objects in our daily life while clustering analysis is an effective tool in the fields of data mining. However, the validity of clustering time series subsequences has been thrown into doubts recently by Keogh et al. In this work, we review this problem and propose the phase shift weighted spherical k-means algorithm (PS-WSKM in abbreviation) for clustering unsynchronized time series. In PS-WSKM, the phase shift procedure is introduced into the clustering process so that the phase problem is solved effectively. Meanwhile, the subsequences weights are assigned to subsequences to make the algorithm more robust. Experimental results on ECG datasets show that our approach is effective for the problem of unsynchronized time series subsequences clustering, which makes contributions to a wide range of applications, particularly in intelligent healthcare.