{"title":"ST-DCONTOUR:一种基于序列密度轮廓的时空聚类方法,用于聚类位置流","authors":"Yongli Zhang, C. Eick","doi":"10.1145/3003421.3003429","DOIUrl":null,"url":null,"abstract":"Spatio-temporal clustering aims to discover interesting regions in spatio-temporal data. In this paper, we propose a novel, serial, density-contour based spatio-temporal clustering algorithm called ST-DCONTOUR which employs a model-based clustering methodology to obtain spatio-temporal clusters from location streams. Our approach subdivides the incoming data into batches and employs a serial approach that generates spatial clusters for each batch first; next, spatio-temporal clusters are formed by identifying continuing relationships between spatial clusters in consecutive batches. Our approach employs contouring algorithms to identify spatial clusters as closed contours of a region where density is above a given threshold, and relies on contour analysis techniques to identify continuing, disappearing, and newly appearing spatial clusters in consecutive batches. We evaluate our approach by conducting a case study involving NYC taxi trips data. The experimental results show that ST-DCONTOUR can discover interesting spatio-temporal patterns in taxi pickup location streams.","PeriodicalId":210363,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"ST-DCONTOUR: a serial, density-contour based spatio-temporal clustering approach to cluster location streams\",\"authors\":\"Yongli Zhang, C. Eick\",\"doi\":\"10.1145/3003421.3003429\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Spatio-temporal clustering aims to discover interesting regions in spatio-temporal data. In this paper, we propose a novel, serial, density-contour based spatio-temporal clustering algorithm called ST-DCONTOUR which employs a model-based clustering methodology to obtain spatio-temporal clusters from location streams. Our approach subdivides the incoming data into batches and employs a serial approach that generates spatial clusters for each batch first; next, spatio-temporal clusters are formed by identifying continuing relationships between spatial clusters in consecutive batches. Our approach employs contouring algorithms to identify spatial clusters as closed contours of a region where density is above a given threshold, and relies on contour analysis techniques to identify continuing, disappearing, and newly appearing spatial clusters in consecutive batches. We evaluate our approach by conducting a case study involving NYC taxi trips data. The experimental results show that ST-DCONTOUR can discover interesting spatio-temporal patterns in taxi pickup location streams.\",\"PeriodicalId\":210363,\"journal\":{\"name\":\"Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3003421.3003429\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3003421.3003429","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ST-DCONTOUR: a serial, density-contour based spatio-temporal clustering approach to cluster location streams
Spatio-temporal clustering aims to discover interesting regions in spatio-temporal data. In this paper, we propose a novel, serial, density-contour based spatio-temporal clustering algorithm called ST-DCONTOUR which employs a model-based clustering methodology to obtain spatio-temporal clusters from location streams. Our approach subdivides the incoming data into batches and employs a serial approach that generates spatial clusters for each batch first; next, spatio-temporal clusters are formed by identifying continuing relationships between spatial clusters in consecutive batches. Our approach employs contouring algorithms to identify spatial clusters as closed contours of a region where density is above a given threshold, and relies on contour analysis techniques to identify continuing, disappearing, and newly appearing spatial clusters in consecutive batches. We evaluate our approach by conducting a case study involving NYC taxi trips data. The experimental results show that ST-DCONTOUR can discover interesting spatio-temporal patterns in taxi pickup location streams.