Data Clustering using Self-Organizing Maps segmented by Mathematic Morphology and Simplified Cluster Validity Indexes: an application in remotely sensed images
This paper presents a cluster analysis method which automatically finds the number of clusters as well as the partitioning of a data set without any type of interaction with the user. The data clustering is made using the self-organizing (or Kohonen) map (SOM). Different partitions of the trained SO...
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| creator | Goncalves, M.L. de Andrade Netto, M.L. Ferreira Costa, J.A. Zullo, J. |
| description | This paper presents a cluster analysis method which automatically finds the number of clusters as well as the partitioning of a data set without any type of interaction with the user. The data clustering is made using the self-organizing (or Kohonen) map (SOM). Different partitions of the trained SOM are obtained from different segmentations of the U-matrix (a neuron-distance image) that are generated by means of mathematical morphology techniques. The different partitions of the trained SOM produce different partitions for the data set which are evaluated by cluster validity indexes. To reduce the computational cost of the cluster analysis process this work also proposes the simplification of cluster validity indexes using the statistical properties of the SOM. The proposed methodology is applied in the cluster analysis of remotely sensed images. |
| doi_str_mv | 10.1109/IJCNN.2006.247043 |
| format | Conference Proceeding |
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The proposed methodology is applied in the cluster analysis of remotely sensed images.</description><subject>Clustering methods</subject><subject>Computational efficiency</subject><subject>Image analysis</subject><subject>Image generation</subject><subject>Image segmentation</subject><subject>Mathematics</subject><subject>Morphology</subject><subject>Satellites</subject><subject>Self organizing feature maps</subject><subject>Sensor systems</subject><issn>2161-4393</issn><issn>2161-4407</issn><isbn>9780780394902</isbn><isbn>0780394909</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1jdlOwzAQRS0WiVL6AYgX_0CKt8QxbyhsRV0eCrxWTj1JjbIpdiXCz_CruKJIoxnNzL3nInRNyZRSom5nr9lyOWWEJFMmJBH8BI0YTWgkBJGnaKJkSkJxJRRhZ_8_rvgFunTukxDGleIj9POgvcZZtXceetuUeO8OfQ1VEa36Ujf2-7AvdOewg7KGxoPB-RAufge19naLF23f7dqqLQesG4PXtu4qW9igO3Lxh66ssX7As8bAF7i7IMS6C7JtILQNtg3uoW49VEOIaVzw2lqX4K7QeaErB5PjHKP3p8e37CWar55n2f08slTGPkopY4SmaUy0KBKhc07zQuVUgtTUGBknLKE6yU2RxirVLKeQg1QKtswIowQfo5s_rgWATdeH9H7YUEkTSRn_BbzNbww</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Goncalves, M.L.</creator><creator>de Andrade Netto, M.L.</creator><creator>Ferreira Costa, J.A.</creator><creator>Zullo, J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2006</creationdate><title>Data Clustering using Self-Organizing Maps segmented by Mathematic Morphology and Simplified Cluster Validity Indexes: an application in remotely sensed images</title><author>Goncalves, M.L. ; de Andrade Netto, M.L. ; Ferreira Costa, J.A. ; Zullo, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-8122018850a4f64ab31bf9b17e7a1dd756261a6bdf8598a2b1ebe799ec2d4d943</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Clustering methods</topic><topic>Computational efficiency</topic><topic>Image analysis</topic><topic>Image generation</topic><topic>Image segmentation</topic><topic>Mathematics</topic><topic>Morphology</topic><topic>Satellites</topic><topic>Self organizing feature maps</topic><topic>Sensor systems</topic><toplevel>online_resources</toplevel><creatorcontrib>Goncalves, M.L.</creatorcontrib><creatorcontrib>de Andrade Netto, M.L.</creatorcontrib><creatorcontrib>Ferreira Costa, J.A.</creatorcontrib><creatorcontrib>Zullo, J.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Goncalves, M.L.</au><au>de Andrade Netto, M.L.</au><au>Ferreira Costa, J.A.</au><au>Zullo, J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Data Clustering using Self-Organizing Maps segmented by Mathematic Morphology and Simplified Cluster Validity Indexes: an application in remotely sensed images</atitle><btitle>The 2006 IEEE International Joint Conference on Neural Network Proceedings</btitle><stitle>IJCNN</stitle><date>2006</date><risdate>2006</risdate><spage>4421</spage><epage>4428</epage><pages>4421-4428</pages><issn>2161-4393</issn><eissn>2161-4407</eissn><isbn>9780780394902</isbn><isbn>0780394909</isbn><abstract>This paper presents a cluster analysis method which automatically finds the number of clusters as well as the partitioning of a data set without any type of interaction with the user. 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| issn | 2161-4393 2161-4407 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Clustering methods Computational efficiency Image analysis Image generation Image segmentation Mathematics Morphology Satellites Self organizing feature maps Sensor systems |
| title | Data Clustering using Self-Organizing Maps segmented by Mathematic Morphology and Simplified Cluster Validity Indexes: an application in remotely sensed images |
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