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Detect irregularly shaped spatio-temporal clusters for decision support

Dong, W.S.; Zhang, Xin; Jiang, Z.B.; Sun, Wei; Xie, Lexing; Hampapur, A.

Description

Many real-world applications call for the use of detecting unusual clusters (abnormal phenomena or significant change) from spatio-temporal data for decision support, e.g., in disease surveillance systems and crime monitoring systems. More accurate detection can offer stronger decision support to enable more effective early warning and efficient resource allocation. Many spatial/spatio-temporal clustering approaches have been designed to detect significantly unusual clusters for decision...[Show more]

dc.contributor.authorDong, W.S.
dc.contributor.authorZhang, Xin
dc.contributor.authorJiang, Z.B.
dc.contributor.authorSun, Wei
dc.contributor.authorXie, Lexing
dc.contributor.authorHampapur, A.
dc.coverage.spatialBeijing
dc.date.accessioned2015-12-13T22:57:51Z
dc.date.createdJuly 10-12 2011
dc.identifier.isbn9781457705731
dc.identifier.urihttp://hdl.handle.net/1885/83167
dc.description.abstractMany real-world applications call for the use of detecting unusual clusters (abnormal phenomena or significant change) from spatio-temporal data for decision support, e.g., in disease surveillance systems and crime monitoring systems. More accurate detection can offer stronger decision support to enable more effective early warning and efficient resource allocation. Many spatial/spatio-temporal clustering approaches have been designed to detect significantly unusual clusters for decision support. In this paper, we focus on more accurately detecting irregularly shaped unusual clusters for point processes and propose a novel approach named EvoGridStatistic. The original problem is mathematically converted to an optimization problem and solved by estimation of distribution algorithm (EDA), which is a powerful global optimization tool. We also propose a prospective spatio-temporal cluster detection approach for surveillance purposes, named EvoGridStatistic-Pro. Experiments verify the effectiveness and efficiency of EvoGridStatistic-Pro over previous approaches. The scalability of our approach is also significantly better than previous ones, which enables EvoGridStatistic-Pro to apply to very large data sets in real-world application systems.
dc.publisherIEEE
dc.relation.ispartofseries2011 IEEE International Conference on Service Operations, Logistics and Informatics, SOLI 2011
dc.sourceProceedings of 2011 IEEE International Conference on Service Operations, Logistics and Informatics, SOLI 2011
dc.subjectKeywords: Cluster detection; Clustering approach; Decision supports; Disease surveillance; Early warning; Efficient resource allocation; Estimation of distribution algorithms; Monitoring system; Optimization problems; Point process; Real-world application; Spatio-t
dc.titleDetect irregularly shaped spatio-temporal clusters for decision support
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2011
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationf5625xPUB11379
local.type.statusPublished Version
local.contributor.affiliationDong, W.S., IBM
local.contributor.affiliationZhang, Xin, IBM
local.contributor.affiliationJiang, Z.B., IBM
local.contributor.affiliationSun, Wei, IBM
local.contributor.affiliationXie, Lexing, College of Engineering and Computer Science, ANU
local.contributor.affiliationHampapur, A., IBM
local.description.embargo2037-12-31
local.bibliographicCitation.startpage231
local.bibliographicCitation.lastpage236
local.identifier.doi10.1109/SOLI.2011.5986561
dc.date.updated2016-02-24T08:38:30Z
local.identifier.scopusID2-s2.0-84859988288
CollectionsANU Research Publications

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