Semi-Supervision Dramatically Improves Time Series Clustering under Dynamic Time Warping
Hoang Anh Dau, Nurjahan Begum, Eamonn Keogh · 2016
The research community seems to have converged in agreement that for time series classification problems, Dynamic Time Warping (DTW)-based nearest-neighbor classifiers are exceptionally hard to beat. Obtaining the best performance from DTW requires setting its only parameter, the warping window width (w). This is typically set by cross validation in the training stage. However, for clustering, by definition we do not have access to such labeled data. This issue seems to have been largely ignored in the literature, with many practitioners simply assuming that "the larger the better" for the value of w, and using as large a value of w as computational resources permit. In this work we show that this is a naive approach which in most circumstances produces inferior clusterings. To address this problem, we introduce a novel semi-supervised technique that allows us to set the best value of w. Unlike virtually all other semi-supervised techniques, our ideas are completely independent of the clustering algorithm used, and can be utilized to improve time series clustering under partitional, hierarchical, spectral or density-based clustering. Our approach requires very little human intervention; moreover, we show that in many cases, true human annotation efforts can be replaced with automatically-generated "pseudo" supervision information. We demonstrate our technique by testing with more than one hundred publicly available datasets.