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dc.contributor.authorRabiei Mamat-
dc.date.accessioned2016-01-02T07:47:56Z-
dc.date.available2016-01-02T07:47:56Z-
dc.date.issued2014-03-
dc.identifier.urihttp://hdl.handle.net/123456789/3772-
dc.description.abstractClustering a set of categorical data into a homogenous class is a fundamental operation in data mining. A number of clustering algorithms have been proposed and have made an important contribution to the issues of clustering especially related to the categorical data. Unfortunately, most of the clustering techniques are not designed to address the issues of uncertainties inherent in the categorical data.en_US
dc.language.isoenen_US
dc.publisherTerengganu: Universiti Malaysia Terengganuen_US
dc.subjectQA 76.9 .D343 R3 2015en_US
dc.subjectRabiei Mamaten_US
dc.subjectThesis University Tun Hussein Onn Malaysiaen_US
dc.subjectData miningen_US
dc.titleMaximum total attribute relative of soft set theory for efficeint catagorical data clusteringen_US
dc.typeThesisen_US
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