Cluster Validation Analysis on Attribute Relative of Soft Set Theory

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dc.contributor.authorRabiei Mamat
dc.contributor.authorAhmad Shukri Mohd Noor
dc.contributor.authorTutut Herawan
dc.date.accessioned2017-10-25T04:01:59Z
dc.date.available2017-10-25T04:01:59Z
dc.date.issued2016
dc.description.abstractData clustering on categorical data pose a difficult challenge since there are no-inherent distance measures between data values. One of the approaches that can be used is by introducing a series of clustering attributes in the categorical data. By this approach, Maximum Total Attribute Relative (MTAR) technique that is based on the attribute relative of soft-set theory has been proposed and proved has better execution time as compared to other equivalent techniques that used the same approach. In this paper, the cluster validity analysis on the technique is explained and discussed. In this analysis, the validity of the clusters produced by MTAR technique is evaluated by the entropy measure using two standards dataset: Soybean (Small) and Zoo from University California at Irvine (UCI) repository. Results show that the clusters produce by MTAR technique have better entropy and improved the clusters validity up to 33%.en_US
dc.identifier.isbn97833195128151
dc.identifier.issnISSN: 2194 5357
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-319-51281-5_1
dc.identifier.urihttp://hdl.handle.net/123456789/7292
dc.language.isoenen_US
dc.publisherSpringer Linken_US
dc.subjectMustafa Mat Derisen_US
dc.subjectAdvances in Intelligent System and Computingen_US
dc.titleCluster Validation Analysis on Attribute Relative of Soft Set Theoryen_US
dc.typeBook chapteren_US
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