A Study on Cluster Validity Measures for Time-Series Data

Abstract

It is difficult to understand the structure of the high-dimensional data. Time-series data is an example of such high-dimensional data. Time-series data requires consideration of differences in period and number of sequences. The choice of dissimilarity and the clustering algorithm also affect the generated cluster structure. In particular, it is difficult to understand the cluster structure of time-series data because of its high dimensionality. The cluster validity measures are useful for evaluating the cluster partition of time series data. In this study, we propose several cluster validity measures for time-series data introducing the dissimilarity of time-series data into cluster validity measures for vector data. It is shown that the shape based distance obtains better results than dynamic time warping through numerical experiments. It is also shown that the trace of the fuzzy covariance matrix is useful for evaluating the cluster partition of time-series data.