SE-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets

Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering. Existing time series clustering methods may fail to capture representative shapelets because they discover shapelets from a large pool of uninformative subsequences, and thus result in low clustering accuracy. This paper proposes a Semi-supervised Clustering of Time Series Using Representative Shapelets (SE-Shapelets) method, which utilizes a small number of labeled and propagated pseudo-labeled time series to help discover representative shapelets, thereby improving the clustering accuracy. In SE-Shapelets, we propose two techniques to discover representative shapelets for the effective clustering of time series. (1) A salient subsequence chain (SSC) that can extract salient subsequences (as candidate shapelets) of a labeled/pseudo-labeled time series, which helps remove massive uninformative subsequences from the pool. (2) A linear discriminant selection (LDS) algorithm to identify shapelets that can capture representative local features of time series in different classes, for convenient clustering. Experiments on UCR time series datasets demonstrate that SE-shapelets discovers representative shapelets and achieves higher clustering accuracy than counterpart semi-supervised time series clustering methods.

Automated summary

This paper proposes a semi-supervised time series clustering method (SE-Shapelets) that utilizes a small number of labeled and propagated pseudo-labeled time series to discover representative shapelets and improve clustering accuracy. The method uses a salient subsequence chain (SSC) and a linear discriminant selection (LDS) algorithm to discover and select shapelets, and experiments show higher clustering accuracy compared to other semi-supervised time series clustering methods.