COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series

TL;DR

This paper introduces COBRAS-TS, a novel semi-supervised clustering method for time series that significantly outperforms existing approaches and can identify complex cluster structures with minimal supervision.

Contribution

The paper adapts the COBRAS clustering framework to time series data, establishing a new state-of-the-art method for semi-supervised clustering in this domain.

Findings

COBRAS-TS outperforms current state-of-the-art methods.

It can identify clusters with separated components.

A small amount of supervision greatly improves clustering quality.

Abstract

Clustering is ubiquitous in data analysis, including analysis of time series. It is inherently subjective: different users may prefer different clusterings for a particular dataset. Semi-supervised clustering addresses this by allowing the user to provide examples of instances that should (not) be in the same cluster. This paper studies semi-supervised clustering in the context of time series. We show that COBRAS, a state-of-the-art semi-supervised clustering method, can be adapted to this setting. We refer to this approach as COBRAS-TS. An extensive experimental evaluation supports the following claims: (1) COBRAS-TS far outperforms the current state of the art in semi-supervised clustering for time series, and thus presents a new baseline for the field; (2) COBRAS-TS can identify clusters with separated components; (3) COBRAS-TS can identify clusters that are characterized by small local patterns; (4) a small amount of semi-supervision can greatly improve clustering quality for time series; (5) the choice of the clustering algorithm matters (contrary to earlier claims in the literature).