Presentation 2014-09-11
Smoothed Data Density Histogram on Self-Organizing Map and Its Application to Cluster Analysis
Yuriko TSUNODA, Kouichiro HAYASHI, Hideaki KAWANO, Hiroshi MAEDA,
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Abstract(in English) This paper proposes an automatic cluster analysis method using self-organizing feature maps (SOM). SOM-based cluster analysis methods are attracted among a lot of studies on the automatic cluster analysis methods. In most situations, a histogram, i.e. a density distribution of data, obtained from the learnt SOM involves more excessive peaks and valleys than we envisioned by the true number of clusters. In this paper, a method to smooth the histogram is proposed to make cluster analysis easier and to eliminate the unwanted extreme values. The effectiveness and the validity of the proposed method for the automatic clustering are examined for several artificially generated data.
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Keyword(in English) self-organizing feature map / cluster analysis / data density histogram / smoothing process
Paper # SIS2014-57
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Conference Date 2014/9/4(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Smoothed Data Density Histogram on Self-Organizing Map and Its Application to Cluster Analysis
Sub Title (in English)
Keyword(1) self-organizing feature map
Keyword(2) cluster analysis
Keyword(3) data density histogram
Keyword(4) smoothing process
1st Author's Name Yuriko TSUNODA
1st Author's Affiliation Kyushu Institute of Technology()
2nd Author's Name Kouichiro HAYASHI
2nd Author's Affiliation Kyushu Institute of Technology
3rd Author's Name Hideaki KAWANO
3rd Author's Affiliation Kyushu Institute of Technology
4th Author's Name Hiroshi MAEDA
4th Author's Affiliation Kyushu Institute of Technology
Date 2014-09-11
Paper # SIS2014-57
Volume (vol) vol.114
Number (no) 205
Page pp.pp.-
#Pages 6
Date of Issue