Presentation	2015-03-05 Document Clustering Method Using Non-negative Matrix Factorization Hazuki KONISHI, Takahide OGAWA,
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Abstract(in English)	In recent years, people have more opportunities to gather information using the Internet. But user can't get information as a user intends or user can't grasp current topics because the result is enormous and overlap. This paper classifies search results in every topic by Document Clustering and uses Non-negative Matrix Factorization (NMF) in Document Clustering. Unfortunately NFM needs the number of clusters before calculation and gives many answers. As a solution for this problem, this paper proposes that resolution is repeated and cluster evaluate using association between document and cluster degree and between word and cluster degree.
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Keyword(in English)	Document Clustering / Non-necative Matrix Factorization / Twitter
Paper #	LOIS2014-65
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Conference Information
Committee	LOIS
Conference Date	2015/2/26(1days)
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Paper Information
Registration To	Life Intelligence and Office Information Systems (LOIS)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Document Clustering Method Using Non-negative Matrix Factorization
Sub Title (in English)
Keyword(1)	Document Clustering
Keyword(2)	Non-necative Matrix Factorization
Keyword(3)	Twitter
1st Author's Name	Hazuki KONISHI
1st Author's Affiliation	Graduate School of Mathematics and Computer Science, Tsuda College()
2nd Author's Name	Takahide OGAWA
2nd Author's Affiliation	Computer Science, Tsuda College
Date	2015-03-05
Paper #	LOIS2014-65
Volume (vol)	vol.114
Number (no)	500
Page	pp.pp.-
#Pages	6
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