Presentation 2008-02-01
On Data Clustering by a Stochastic Embedding Method
Naoto NISHIKAWA, Shinji DOI, Sadatoshi KUMAGAI,
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Abstract(in English) The stochastic proximity embedding (SPE) is one of the data clustering methods which use given similarity among input data. The result of data clustering by the SPE depends on the definition of similarity. In this paper, we propose a method which automatically optimizes the similarity for data clustering by an iterative learning and extend the SPE by applying the process of the iterative learning. Using some low-dimensional artificial data and high-dimensional practical data, we demonstrate the effectiveness of the extended SPE.
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Keyword(in English) data mining / data clustering / stochastic proximity embedding (SPE)
Paper # NLP2007-149
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Conference Information
Committee NLP
Conference Date 2008/1/25(1days)
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Paper Information
Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) On Data Clustering by a Stochastic Embedding Method
Sub Title (in English)
Keyword(1) data mining
Keyword(2) data clustering
Keyword(3) stochastic proximity embedding (SPE)
1st Author's Name Naoto NISHIKAWA
1st Author's Affiliation Division of Electrical, Electronic and Information Engineering, Graduate School of Engineering, Osaka University()
2nd Author's Name Shinji DOI
2nd Author's Affiliation Division of Electrical, Electronic and Information Engineering, Graduate School of Engineering, Osaka University
3rd Author's Name Sadatoshi KUMAGAI
3rd Author's Affiliation Division of Electrical, Electronic and Information Engineering, Graduate School of Engineering, Osaka University
Date 2008-02-01
Paper # NLP2007-149
Volume (vol) vol.107
Number (no) 478
Page pp.pp.-
#Pages 6
Date of Issue