Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
Identifieur interne : 002230 ( Main/Exploration ); précédent : 002229; suivant : 002231Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
Auteurs : Elias Pampalk [Autriche] ; Andreas Rauber [Autriche] ; Dieter Merkl [Autriche]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2002.
Abstract
Abstract: Several methods to visualize clusters in high-dimensional data sets using the Self-Organizing Map (SOM) have been proposed. However, most of these methods only focus on the information extracted from the model vectors of the SOM. This paper introduces a novel method to visualize the clusters of a SOM based on smoothed data histograms. The method is illustrated using a simple 2-dimensional data set and similarities to other SOM based visualizations and to the posterior probability distribution of the Generative Topographic Mapping are discussed. Furthermore, the method is evaluated on a real world data set consisting of pieces of music.
Url:
DOI: 10.1007/3-540-46084-5_141
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: 003267
- to stream Istex, to step Curation: 002E04
- to stream Istex, to step Checkpoint: 001A97
- to stream Main, to step Merge: 002276
- to stream Main, to step Curation: 002230
Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps</title>
<author><name sortKey="Pampalk, Elias" sort="Pampalk, Elias" uniqKey="Pampalk E" first="Elias" last="Pampalk">Elias Pampalk</name>
</author>
<author><name sortKey="Rauber, Andreas" sort="Rauber, Andreas" uniqKey="Rauber A" first="Andreas" last="Rauber">Andreas Rauber</name>
</author>
<author><name sortKey="Merkl, Dieter" sort="Merkl, Dieter" uniqKey="Merkl D" first="Dieter" last="Merkl">Dieter Merkl</name>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">ISTEX</idno>
<idno type="RBID">ISTEX:7FF6D08D691E1877544616D9F2A5E119B8409A67</idno>
<date when="2002" year="2002">2002</date>
<idno type="doi">10.1007/3-540-46084-5_141</idno>
<idno type="url">https://api.istex.fr/document/7FF6D08D691E1877544616D9F2A5E119B8409A67/fulltext/pdf</idno>
<idno type="wicri:Area/Istex/Corpus">003267</idno>
<idno type="wicri:Area/Istex/Curation">002E04</idno>
<idno type="wicri:Area/Istex/Checkpoint">001A97</idno>
<idno type="wicri:doubleKey">0302-9743:2002:Pampalk E:using:smoothed:data</idno>
<idno type="wicri:Area/Main/Merge">002276</idno>
<idno type="wicri:Area/Main/Curation">002230</idno>
<idno type="wicri:Area/Main/Exploration">002230</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps</title>
<author><name sortKey="Pampalk, Elias" sort="Pampalk, Elias" uniqKey="Pampalk E" first="Elias" last="Pampalk">Elias Pampalk</name>
<affiliation wicri:level="3"><country xml:lang="fr">Autriche</country>
<wicri:regionArea>Austrian Research Institute for Artificial Intelligence (OeFAI), Schottengasse 3, A-1010, Vienna</wicri:regionArea>
<placeName><settlement type="city">Vienne (Autriche)</settlement>
<region nuts="2" type="province">Vienne (Autriche)</region>
</placeName>
</affiliation>
<affiliation wicri:level="1"><country wicri:rule="url">Autriche</country>
</affiliation>
</author>
<author><name sortKey="Rauber, Andreas" sort="Rauber, Andreas" uniqKey="Rauber A" first="Andreas" last="Rauber">Andreas Rauber</name>
<affiliation wicri:level="3"><country xml:lang="fr">Autriche</country>
<wicri:regionArea>Department of Software Technology and Interactive Systems, Vienna University of Technology, Favoritenstr. 9-11/188, A-1040, Vienna</wicri:regionArea>
<placeName><settlement type="city">Vienne (Autriche)</settlement>
<region nuts="2" type="province">Vienne (Autriche)</region>
</placeName>
</affiliation>
<affiliation wicri:level="1"><country wicri:rule="url">Autriche</country>
</affiliation>
</author>
<author><name sortKey="Merkl, Dieter" sort="Merkl, Dieter" uniqKey="Merkl D" first="Dieter" last="Merkl">Dieter Merkl</name>
<affiliation wicri:level="3"><country xml:lang="fr">Autriche</country>
<wicri:regionArea>Department of Software Technology and Interactive Systems, Vienna University of Technology, Favoritenstr. 9-11/188, A-1040, Vienna</wicri:regionArea>
<placeName><settlement type="city">Vienne (Autriche)</settlement>
<region nuts="2" type="province">Vienne (Autriche)</region>
</placeName>
</affiliation>
<affiliation wicri:level="1"><country wicri:rule="url">Autriche</country>
</affiliation>
</author>
</analytic>
<monogr></monogr>
<series><title level="s">Lecture Notes in Computer Science</title>
<imprint><date>2002</date>
</imprint>
<idno type="ISSN">0302-9743</idno>
</series>
<idno type="istex">7FF6D08D691E1877544616D9F2A5E119B8409A67</idno>
<idno type="DOI">10.1007/3-540-46084-5_141</idno>
<idno type="ChapterID">Chap141</idno>
<idno type="ChapterID">141</idno>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
<langUsage><language ident="en">en</language>
</langUsage>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">Abstract: Several methods to visualize clusters in high-dimensional data sets using the Self-Organizing Map (SOM) have been proposed. However, most of these methods only focus on the information extracted from the model vectors of the SOM. This paper introduces a novel method to visualize the clusters of a SOM based on smoothed data histograms. The method is illustrated using a simple 2-dimensional data set and similarities to other SOM based visualizations and to the posterior probability distribution of the Generative Topographic Mapping are discussed. Furthermore, the method is evaluated on a real world data set consisting of pieces of music.</div>
</front>
</TEI>
<affiliations><list><country><li>Autriche</li>
</country>
<region><li>Vienne (Autriche)</li>
</region>
<settlement><li>Vienne (Autriche)</li>
</settlement>
</list>
<tree><country name="Autriche"><region name="Vienne (Autriche)"><name sortKey="Pampalk, Elias" sort="Pampalk, Elias" uniqKey="Pampalk E" first="Elias" last="Pampalk">Elias Pampalk</name>
</region>
<name sortKey="Merkl, Dieter" sort="Merkl, Dieter" uniqKey="Merkl D" first="Dieter" last="Merkl">Dieter Merkl</name>
<name sortKey="Merkl, Dieter" sort="Merkl, Dieter" uniqKey="Merkl D" first="Dieter" last="Merkl">Dieter Merkl</name>
<name sortKey="Pampalk, Elias" sort="Pampalk, Elias" uniqKey="Pampalk E" first="Elias" last="Pampalk">Elias Pampalk</name>
<name sortKey="Rauber, Andreas" sort="Rauber, Andreas" uniqKey="Rauber A" first="Andreas" last="Rauber">Andreas Rauber</name>
<name sortKey="Rauber, Andreas" sort="Rauber, Andreas" uniqKey="Rauber A" first="Andreas" last="Rauber">Andreas Rauber</name>
</country>
</tree>
</affiliations>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Musique/explor/MozartV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 002230 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 002230 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Wicri/Musique |area= MozartV1 |flux= Main |étape= Exploration |type= RBID |clé= ISTEX:7FF6D08D691E1877544616D9F2A5E119B8409A67 |texte= Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps }}
This area was generated with Dilib version V0.6.20. |