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Clustering Technique using Concepts for Web Documents

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dc.contributor.author Tar, Hmway Hmway
dc.contributor.author Nyunt, Thi Thi Soe
dc.date.accessioned 2019-07-25T05:17:55Z
dc.date.available 2019-07-25T05:17:55Z
dc.date.issued 2010-12-16
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1282
dc.description.abstract Web document clustering becomes an essential technology with the popularity of the Internet. That also means that fast and high-quality document clustering techniques play core topics. One of the main issues for clustering is the feature selection for the documents. The selected features should contain sufficient or more reliable information about original web documents. Feature selection is important because some of the irrelevant or redundant feature may misguide the clustering result. To counteract this issue, this paper proposes the concept weight for feature selection which can improve the efficiency and accuracy of clustering. The system is designed to perform document preprocessing, weight estimation and clustering process that uses the term weight and semantic weight. This paper introduces a method which proposed the concept weight for clustering process. en_US
dc.language.iso en en_US
dc.publisher Fifth Local Conference on Parallel and Soft Computing en_US
dc.subject Clustering en_US
dc.subject feature selection en_US
dc.subject concept weight en_US
dc.title Clustering Technique using Concepts for Web Documents en_US
dc.type Article en_US

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