Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/1738
DC FieldValueLanguage
dc.contributor.authorDagher, Issamen_US
dc.date.accessioned2020-12-23T08:58:31Z-
dc.date.available2020-12-23T08:58:31Z-
dc.date.issued2012-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/1738-
dc.description.abstractClustering analysis is the process of separating data according to some similarity measure. A cluster consists of data which are more similar to each other than to other clusters. The similarity of a datum to a certain cluster can be defined as the distance of that datum to the prototype of that cluster. Typically, the prototype of a cluster is a real vector that is called the center of that cluster. In this paper, the prototype of a cluster is generalized to be a complex vector (complex center). A new distance measure is introduced. New formulas for the fuzzy membership and the fuzzy covariance matrix are introduced. Cluster validity measures are used to assess the goodness of the partitions obtained by the complex centers compared those obtained by the real centers. The validity measures used in this paper are the partition coefficient, classification entropy, partition index, separation index, Xie and Benis index, and Dunns index. It is shown in this paper that clustering with complex prototypes will give better partitions of the data than using real prototypes.en_US
dc.format.extent11 p.en_US
dc.language.isoengen_US
dc.subjectClusteringen_US
dc.subjectPrototypeen_US
dc.subjectComplex centeren_US
dc.titleClustering with complex centersen_US
dc.typeJournal Articleen_US
dc.contributor.affiliationDepartment of Computer Engineeringen_US
dc.description.volume21en_US
dc.description.issue1en_US
dc.description.startpage133en_US
dc.description.endpage144en_US
dc.date.catalogued2017-11-09-
dc.description.statusPublisheden_US
dc.identifier.ezproxyURLhttp://ezsecureaccess.balamand.edu.lb/login?url=https://link.springer.com/article/10.1007/s00521-011-0616-4en_US
dc.identifier.OlibID174877-
dc.relation.ispartoftextNeural computing and applicationsen_US
dc.provenance.recordsourceOliben_US
Appears in Collections:Department of Computer Engineering
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