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  1. Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '16)
  2. ST-DCONTOUR: a serial, density-contour based spatio-temporal clustering approach to cluster location streams
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Formalization of network-constrained moving object queries with application to benchmarking
Continuous detection of black holes for moving objects at sea
Categorizing spatiotemporal aggregates for moving regions
On computing temporal functions for time-dependent networks using trajectory data streams
ST-DCONTOUR: a serial, density-contour based spatio-temporal clustering approach to cluster location streams
A survey of techniques and open-source tools for processing streams of spatio-temporal events
A general feature-based map matching framework with trajectory simplification
MobiDict: a mobility prediction system leveraging realtime location data streams
Mining frequent episodes from multivariate spatiotemporal event sequences
Mining frequent trajectory patterns from online footprints

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ST-DCONTOUR: a serial, density-contour based spatio-temporal clustering approach to cluster location streams

Content Provider ACM Digital Library
Author Zhang, Yongli Eick, Christoph F.
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.
Starting Page 1
Ending Page 4
Page Count 4
File Format PDF
ISBN 9781450345798
DOI 10.1145/3003421.3003429
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-10-31
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Model-based clustering Density contour analysis Stream processing Spatio-temporal clustering Density contour
Content Type Text
Resource Type Article
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