Presentation | 2008-02-01 On Data Clustering by a Stochastic Embedding Method Naoto NISHIKAWA, Shinji DOI, Sadatoshi KUMAGAI, |
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Abstract(in Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | data mining / data clustering / stochastic proximity embedding (SPE) |
Paper # | NLP2007-149 |
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Committee | NLP |
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Conference Date | 2008/1/25(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Nonlinear Problems (NLP) |
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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 |
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