Incremental fuzzy clustering of time series

Abstract

Clustering is one of the most popular data mining methods for analyzing the time series, not only due to its exploratory power, but also because it is often a preprocessing step or subroutine for other techniques. In this paper, an incremental fuzzy clustering algorithm of time series (IFCTS) is proposed, in which the clustering is divided into two stages: offline and online clustering. During the offline clustering process, the fuzzy clustering validity evaluation index is introduced to FCM to automatically obtain the optimal number of initial clusters for off-line data. Then, in the online clustering process, IFCTS algorithm can dynamically update the existing clusters in the incremental data snapshot, distinguish outliers and control the creation of new clusters adaptively by combining with the previous clusters. The experimental results show that the proposed algorithm has good clustering accuracy and efficiency for both equal-length and unequal-length time series.