{"title":"Simple, fast, and accurate clustering of data sequences","authors":"Luis A. Leiva, E. Vidal","doi":"10.1145/2166966.2167027","DOIUrl":null,"url":null,"abstract":"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).","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IUI. International Conference on Intelligent User Interfaces","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2166966.2167027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
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).