Frobenius correlation based u-shapelets discovery for time series clustering

PATTERN RECOGNITION

卷:103

Article

Fotso, Vanel Steve Siyou¹  Nguifo, Engelbert Mephu¹  Vaslin, Philippe¹ 

[1] Univ Clermont Auvergne, LIMOS, CNRS, F-63000 Clermont Ferrand, France

关键词: Clustering;    UShapelet;    Correlation;    Time series;   

DOI  :  10.1016/j.patcog.2020.107301

来源: Elsevier

【 摘 要 】

An u-shapelet is a sub-sequence of a time series used for the clustering of time series datasets. The purpose of this paper is to discover u-shapelets on uncertain time series. To achieve this goal, we propose a dissimilarity score called FOTS whose computation is based on the eigenvector decomposition and the comparison of the autocorrelation matrices of the time series. This score is robust to the presence of uncertainty; it is not very sensitive to transient changes; it allows capturing complex relationships between time series such as oscillations and trends, and it is also well adapted to the comparison of short time series. The FOTS score is used with the Scalable Unsupervised Shapelet Discovery algorithm for the clustering of 63 datasets, and it has shown a substantial improvement in the quality of the clustering with respect to the Rand Index. This work defines a novel framework for the clustering of uncertain time series. (C) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

Free