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

arXiv: https://arxiv.org/abs/2304.03292 · HTML: https://mlsanity.com/paper/2304.03292/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 48 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation](https://mlsanity.com/paper/2206.08948/) (2022)
- [Ultra-Scalable Spectral Clustering and Ensemble Clustering](https://mlsanity.com/paper/1903.01057/) (2019)
- [Causal-based Time Series Domain Generalization for Vehicle Intention Prediction](https://mlsanity.com/paper/2112.02093/) (2021)
- [A Review and Evaluation of Elastic Distance Functions for Time Series Clustering](https://mlsanity.com/paper/2205.15181/) (2022)
- [Time series clustering based on the characterisation of segment typologies](https://mlsanity.com/paper/1810.11624/) (2018)
- [COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series](https://mlsanity.com/paper/1805.00779/) (2018)
- [AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering](https://mlsanity.com/paper/2208.04313/) (2022)

## Built on by (most cited first, 1 of 1)

- [DiaSeg: Diagonal Segment Extraction from DTW Paths for Interpretable Gait Analysis](https://mlsanity.com/paper/2609.24223/) (2026)

## Abstract (arXiv)

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 textitsalient 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 textitlinear 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.