A Study on Cluster Validity Measures Based on Fuzzy Membership 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 a cluster validity measure based on fuzzy membership for time-series data. It is shown that the proposed method performs as well as or better than existing methods through numerical experiments. It is also shown that the proposed method is useful for data that is close to the cluster center to which each data belongs and far from other cluster centers.