A clustering procedure for exploratory mining of vector time series

Authors:
T. Warren Liao
Affiliations:
Industrial and Manufacturing Systems Engineering Department, Louisiana State University, Baton Rouge, LA 70803, USA
Venue:
Pattern Recognition
Year:
2007

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Abstract

A two-step procedure is developed for the exploratory mining of real-valued vector (multivariate) time series using partition-based clustering methods. The proposed procedure was tested with model-generated data, multiple sensor-based process data, as well as simulation data. The test results indicate that the proposed procedure is quite effective in producing better clustering results than a hidden Markov model (HMM)-based clustering method if there is a priori knowledge about the number of clusters in the data. Two existing validity indices were tested and found ineffective in determining the actual number of clusters. Determining the appropriate number of clusters in the case that there is no a priori knowledge is a known unresolved research issue not only for our proposed procedure but also for the HMM-based clustering method and further development is necessary.