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Data mining with clustering methods

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dc.contributor.advisor Prof. Huynh Ngoc Phien (Chairperson) en_US
dc.contributor.advisor Prof. Phan Minh Dung (Member) en_US
dc.contributor.advisor Dr. Hoang Le Tien (Member) en_US
dc.contributor.author Tatpong Katanyukul en_US
dc.date December 2000 en_US
dc.date.accessioned 2015-01-12T10:39:15Z
dc.date.available 2015-01-12T10:39:15Z
dc.identifier.other AIT Thesis no.CS-00-9 en_US
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/179
dc.description 54 leaves en_US
dc.description.abstract Clustering is the important preliminary task for data mining. Its result is useful for data reduction and hypothesis extraction from data. The complication of data characteristics can degrade the clustering performance. Multivariate time-series data is one of the complications. In the study, a new method for graphical display was proposed for efficient representation. Three classical clustering algorithms and the self-organizing map with three ways of distance measurement were applied to cluster the multivariate time-series data set obtained from the UCI KDD Archive. The results were inspected by graphical views accompanied with the statistical figures. It was shown that the proposed method can display the results obtained from the four different clustering methods very well. It was found that the Euclidean distance performs very satisfactorily for the data set employed. en_US
dc.description.sponsorship Royal Thai Government en_US
dc.relation.ispartof Thesis no. CS-00-9 en_US
dc.relation.ispartof Asian Institute of Technology. Thesis no. CS-00-9 en_US
dc.source SAT/CS en_US
dc.subject Cluster set theory en_US
dc.subject Data mining en_US
dc.title Data mining with clustering methods en_US
dc.type Thesis en_US

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