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Shapelets that discriminate time series using local features (subsequences)are promising for time series clustering. Existing time series clusteringmethods may fail to capture representative shapelets because they discovershapelets from a large pool of uninformative subsequences, and thus result inlow clustering accuracy. This paper proposes a Semi-supervised Clustering ofTime Series Using Representative Shapelets (SE-Shapelets) method, whichutilizes a small number of labeled and propagated pseudo-labeled time series tohelp discover representative shapelets, thereby improving the clusteringaccuracy. In SE-Shapelets, we propose two techniques to discover representativeshapelets for the effective clustering of time series. 1) A \textit{salientsubsequence chain} ($SSC$) that can extract salient subsequences (as candidateshapelets) of a labeled/pseudo-labeled time series, which helps remove massiveuninformative subsequences from the pool. 2) A \textit{linear discriminantselection} ($LDS$) algorithm to identify shapelets that can capturerepresentative local features of time series in different classes, forconvenient clustering. Experiments on UCR time series datasets demonstrate thatSE-shapelets discovers representative shapelets and achieves higher clusteringaccuracy than counterpart semi-supervised time series clustering methods.

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