St-Copot: Spatio-Temporal Clustering With Contour Polygon Trees
25TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2017)(2017)
摘要
Nowadays, growing effort has been put to develop spatio-temporal clustering approaches that are capable of discovering interesting patterns in large spatio-temporal data streams. In this paper, we propose a 3-phase serial, density-contour based clustering algorithm called ST-COPOT, which can identify spatio-temporal cluster at multiple levels of density granularity. ST-COPOT takes the point cloud data as input and divides it into batches, next, it employs a non-parametric kernel density estimation approach and contouring algorithms to obtain spatial clusters; at last, spatio-temporal clusters are formed by identifying continuing relationships between spatial clusters in consecutive batches. Moreover, a novel data structure called contour polygon tree is introduced as a compact representation of the spatial clusters obtained for each batch for different density thresholds, and a family of novel distance functions that operate on contour polygon trees are proposed to identify continuing clusters. The experimental results on NYC taxi trips data show that ST-COPOT can effectively discover interesting spatio-temporal patterns in taxi pickup location streams.
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关键词
Spatio-temporal point cloud data,spatio-temporal clustering,spatio-temporal data stream,contour polygon tree
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