Exploiting data topology in visualization and clustering of self-organizing maps

dc.contributor.author	Taşdemir, K.
dc.contributor.author	Merényi, E.
dc.date.accessioned	2021-01-25T20:51:54Z
dc.date.available	2021-01-25T20:51:54Z
dc.date.issued	2009
dc.identifier	10.1109/TNN.2008.2005409
dc.identifier.issn	10459227
dc.identifier.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-67349242966&doi=10.1109%2fTNN.2008.2005409&partnerID=40&md5=aa16fff68677524bae851a44f4ddcfd5
dc.identifier.uri	https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/10721
dc.description.abstract	The self-organizing map (SOM) is a powerful method for visualization, cluster extraction, and data mining. It has been used successfully for data of high dimensionality and complexity where traditional methods may often be insufficient. In order to analyz
dc.language.iso	English
dc.publisher	IEEE Transactions on Neural Networks
dc.title	Exploiting data topology in visualization and clustering of self-organizing maps
dc.type	Article
dc.relation.firstpage	549
dc.relation.lastpage	562
dc.relation.volume	20
dc.relation.issue	4
dc.description.affiliations	Electrical and Computer Engineering Department, Rice University, Houston, TX 77005, United States; Computer Engineering Department, Yasar University, Bornova, Izmir 35100, Turkey

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