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Searching data clusters in spatial data sets using R-tree

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dc.contributor.advisor Prof. Ramakoti Sadananda (Chairperson) en_US
dc.contributor.advisor Dr. Qi Yulu (Member) en_US
dc.contributor.advisor Dr. Nitin V Afzulpurkar (Member) en_US
dc.contributor.author Tai, Xin en_US
dc.date December 2000 en_US
dc.date.accessioned 2015-01-12T10:39:21Z
dc.date.available 2015-01-12T10:39:21Z
dc.identifier.other AIT Thesis no.CS-00-34 en_US
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/204
dc.description 55 leaves en_US
dc.description.abstract Data clustering is regarded as a particular branch of data mining. In this thesis, data clustering refers to the problem of dividing N data points into K groups to minimize the intraà à à à à ­group difference, such as the sum of the squared distances from the cluster centers. In this thesis study, I deal with the important characteristics of clustering in spatial knowledge discovery. In this approach, I studied various data clustering methods, and analyze properties and features of the data clusters in spatial data sets. And, on the basis of the existing methods of B-tree, R-tree and R*-tree in spatial data mining, I built up an efficient and smart Clustering Feature Entry R-tree that is an improvement over an R-tree method. The CFE R-tree reduces the problem of clustering the original data points into a simpler and smaller one of clustering the clustered sub-clusters. And, experimentally, I also use some spatial data sets to implement the data clustering searching by using the modified CFE R-tree. en_US
dc.description.sponsorship State Education Council, P. R. China en_US
dc.relation.ispartof Thesis no. CS-00-34 en_US
dc.relation.ispartof Asian Institute of Technology. Thesis no. CS-00-34 en_US
dc.source SAT/CS en_US
dc.subject Data mining en_US
dc.title Searching data clusters in spatial data sets using R-tree en_US
dc.type Thesis en_US

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