Presentation	2014-09-11 Food text clustering by selecting the most representative word Sosuke Amano, Kiyoharu AIZAWA, Makoto OGAWA,
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Abstract(in English)	The development of information technology is enable us to collect the big data of food information. Effective ways of analyzing such food data are needed. In this paper, we propose a method that cluster meal names by selecting the most representative word of meal categories.
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Keyword(in English)	Food log / Document clustering / short text
Paper #	DE2014-29
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Committee	DE
Conference Date	2014/9/3(1days)
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Paper Information
Registration To	Data Engineering (DE)
Language	JPN
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Title (in English)	Food text clustering by selecting the most representative word
Sub Title (in English)
Keyword(1)	Food log
Keyword(2)	Document clustering
Keyword(3)	short text
1st Author's Name	Sosuke Amano
1st Author's Affiliation	Grad. School of Interdisciplinary Information Studies, The University of Tokyo:Dept. of Information and Communication Eng., The University of Tokyo()
2nd Author's Name	Kiyoharu AIZAWA
2nd Author's Affiliation	Grad. School of Interdisciplinary Information Studies, The University of Tokyo:Dept. of Information and Communication Eng., The University of Tokyo
3rd Author's Name	Makoto OGAWA
3rd Author's Affiliation	foo.log, Inc.
Date	2014-09-11
Paper #	DE2014-29
Volume (vol)	vol.114
Number (no)	204
Page	pp.pp.-
#Pages	4
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