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Clustering blogs using document context similarity and spectral graph partitioning
Abstract Semantic-based document clustering has beeSemantic-based document clustering has been a challenging problem over the past few years and its execution depends on modeling the underlying content and its similarity metrics. Existing metrics evaluate pair wise text similarity based on text content, which is referred as content similarity. The performances of these measures are based on co-occurrences, and ignore the semantics among words. Although, several research works have been carried out to solve this problem, we propose a novel similarity measure by exploiting external knowledge base-Wikipedia to enhance document clustering task. Wikipedia articles and the main categories were used to predict and affiliate them to their semantic concepts. In this measure, we incorporate context similarity by constructing a vector with each dimension representing contents similarity between a document and other documents in the collection. Experimental result conducted on TREC blog dataset confirms that the use of context similarity measure, can improve the precision of document clustering significantly.sion of document clustering significantly.
Abstractsub Semantic-based document clustering has beeSemantic-based document clustering has been a challenging problem over the past few years and its execution depends on modeling the underlying content and its similarity metrics. Existing metrics evaluate pair wise text similarity based on text content, which is referred as content similarity. The performances of these measures are based on co-occurrences, and ignore the semantics among words. Although, several research works have been carried out to solve this problem, we propose a novel similarity measure by exploiting external knowledge base-Wikipedia to enhance document clustering task. Wikipedia articles and the main categories were used to predict and affiliate them to their semantic concepts. In this measure, we incorporate context similarity by constructing a vector with each dimension representing contents similarity between a document and other documents in the collection. Experimental result conducted on TREC blog dataset confirms that the use of context similarity measure, can improve the precision of document clustering significantly.sion of document clustering significantly.
Bibtextype inproceedings  +
Doi 10.1007/978-3-642-25661-5_60  +
Has author Ayyasamy R.K. + , Alhashmi S.M. + , Eu-Gene S. + , Tahayna B. +
Has editor Wang Y.Li T. +
Has extra keyword As content + , Bipartite graphs + , Blog Clustering + , Dataset + , Document Clustering + , Document context + , External knowledge + , Semantic concept + , Similarity measure + , Similarity metrics + , Spectral graph partitioning + , Text content + , Text similarity + , Wikipedia + , Cluster analysis + , Graph theory + , Information retrieval + , Intelligent systems + , Knowledge based systems + , Knowledge engineering + , Semantics + , Internet +
Has keyword Bipartite graph + , Blog Clustering + , Similarity measure + , Wikipedia +
Isbn 9783642256608  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 475–486  +
Published in Advances in Intelligent and Soft Computing +
Title Clustering blogs using document context similarity and spectral graph partitioning +
Type conference paper  +
Volume 123  +
Year 2011 +
Creation dateThis property is a special property in this wiki. 7 November 2014 03:37:57  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 03:37:57  +
DateThis property is a special property in this wiki. 2011  +
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