Simple, fast, and accurate clustering of data sequences
Luis A. Leiva, Enrique Vidal · 2012
Many devices generate large amounts of data that follow some sort of sequentiality, e.g., motion sensors, e-pens, or eye trackers, and therefore these data often need to be compressed for classification, storage, and/or retrieval purposes. This paper introduces a simple, accurate, and extremely fast technique inspired by the well-known K-means algorithm to properly cluster sequential data. We illustrate the feasibility of our algorithm on a web-based prototype that works with trajectories derived from mouse and touch input. As can be observed, our proposal outperforms the classical K-means algorithm in terms of accuracy (better, well-formed segmentations) and performance (less computation time).