A new shape-based clustering algorithm for time series

Time series clustering is a research hotspot in data mining. Most of the existing clustering algorithms combine with the classical distance measure which ignore the offset of sequence shape. As a result, shape-based clustering algorithms are becoming increasingly popular. On the majority of data sets, the most representative shape-based clustering algo-rithm, KShape, which defines a shape-based distance with shift invariance, has been shown to outperform other algorithms.In this paper, we propose a new shape-based clustering algorithm named Fractional Order Shape-based k-cluster(FrOKShape), which defines a multi-variable shape-based dis-tance by normalized fractional order cross-correlation and uses the DTW Barycenter Averaging (DBA) as a center computation strategy. Our distance exhibits excellent shape shift deviating properties and good compatibility integrated with a variety of existing clus-tering center strategies so that it can provide more potential good results. Experiments show that combining our distance with a traditional clustering algorithm produces excel-lent clustering indicators. In a series of comparative experiments, FrOKShape also exhibits a comparable result to the existing better shape-based clustering algorithm KShape.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper discusses the research on time series clustering and the importance of shape-based clustering algorithms. It proposes a new algorithm called FrOKShape, which shows excellent results when combined with traditional clustering algorithms and comparable performance to existing algorithms.