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Please use this identifier to cite or link to this item: http://scholars.ntou.edu.tw/handle/123456789/17045
DC FieldValueLanguage
dc.contributor.authorChih-Wen Wuen_US
dc.contributor.authorJin-Lian Chenen_US
dc.contributor.authorJung-Hua Wangen_US
dc.date.accessioned2021-06-07T06:07:15Z-
dc.date.available2021-06-07T06:07:15Z-
dc.date.issued2001-10-
dc.identifier.issn1062-922X-
dc.identifier.urihttp://scholars.ntou.edu.tw/handle/123456789/17045-
dc.description.abstractA self-organizing mountain method (SOMM) is presented. SOMM incorporates the Possibilistic C-Means (PCM) technique and the concept of the mountain method to perform clustering. By self-organizing we mean that parameters are data-driven, the terrain of each cluster (or mountain) is estimated, the precise center of each cluster and the terminating condition are determined by the input nature. In addition, SOMM is robust even when a large number of outliers/noises is presented. The simulation results show that the robust clustering can be obtained for various Gaussian clusters and uniform clusters, respectively.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.titleSelf-organizing mountain method for clusteringen_US
dc.typeconference paperen_US
dc.relation.conference2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspaceen_US
dc.relation.conferenceTucson, AZ, USAen_US
dc.identifier.doi10.1109/ICSMC.2001.972922-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.fulltextno fulltext-
item.grantfulltextnone-
item.openairetypeconference paper-
crisitem.author.deptCollege of Electrical Engineering and Computer Science-
crisitem.author.deptDepartment of Electrical Engineering-
crisitem.author.deptNational Taiwan Ocean University,NTOU-
crisitem.author.parentorgNational Taiwan Ocean University,NTOU-
crisitem.author.parentorgCollege of Electrical Engineering and Computer Science-
Appears in Collections:電機工程學系
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