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dc.contributor.authorLiao, Zhung-Xunen_US
dc.contributor.authorPeng, Wen-Chihen_US
dc.date.accessioned2014-12-08T15:22:44Z-
dc.date.available2014-12-08T15:22:44Z-
dc.date.issued2012-04-01en_US
dc.identifier.issn0219-1377en_US
dc.identifier.urihttp://hdl.handle.net/11536/16062-
dc.description.abstractSpatial data objects that possess attributes in the optimization domain and the geographic domain are now widely available. For example, sensor data are one kind of spatial data objects. The location of a sensor is an attribute in the geographic domain, while its reading is an attribute in the optimization domain. Previous studies discuss dual clustering problems that attempt to partition spatial data objects into several groups, such that objects in the same group have similar values in their optimization attributes and form a compact region in the geographic domain. However, previous studies do not clearly define compact regions. Therefore, this paper formulates a connective dual clustering problem with an explicit connected constraint given. Objects with a geographic distance smaller than or equal to the connected constraint are connected. The goal of the connective dual clustering problem is to derive clusters that contain objects with similar values in the optimization domain and are connected in the geographic domain. This study further proposes an algorithm CLS (Clustering with Local Search) to efficiently derive clusters. This algorithm consists of two phases: the ConGraph (standing for Connective Graph) transformation phase and the clustering phase. In the ConGraph transformation phase, CLS first transforms the data objects into a ConGraph that captures geographic constraints among data objects and selects initial seeds for clustering. Then, the initial seeds selected nearby data objects and formed coarse clusters by exploring local search in the clustering phase. Moreover, coarse clusters are merged and finely turned. Experiments show that CLS algorithm is more efficient and scalable than existing methods.en_US
dc.language.isoen_USen_US
dc.subjectDual clusteringen_US
dc.subjectSpatial clusteringen_US
dc.subjectSpatial data miningen_US
dc.titleClustering spatial data with a geographic constraint: exploring local searchen_US
dc.typeArticleen_US
dc.identifier.journalKNOWLEDGE AND INFORMATION SYSTEMSen_US
dc.citation.volume31en_US
dc.citation.issue1en_US
dc.citation.epage153en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000303129500007-
dc.citation.woscount4-
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