k-CEVCLUS: Constrained evidential clustering of large dissimilarity data

In evidential clustering, cluster-membership uncertainty is represented by Dempster–Shafer mass functions. The EVCLUS algorithm is an evidential clustering procedure for dissimilarity data, based on the assumption that similar objects should be assigned mass functions with low degree of conflict. CE...

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Veröffentlicht in:	Knowledge-based systems Jg. 142; S. 29 - 44
Hauptverfasser:	Li, Feng, Li, Shoumei, Denœux, Thierry
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 15.02.2018 Elsevier Science Ltd Elsevier
Schlagworte:	Algorithms Artificial Intelligence Belief functions Cluster analysis Clustering Computer Science Constrained clustering Constraints Credal partition Datasets Dempster–Shafer theory Evidence theory Evidentiality Expert systems Instance-level constraints Membership Relational data Relational data bases Storage Uncertainty Credal partition Belief functions Evidence theory Relational data Constrained clustering Dempster–Shafer theory Instance-level constraints relational data belief functions Dempster-Shafer theory credal partition constraints constrained clustering instance-level
ISSN:	0950-7051, 1872-7409
Online-Zugang:	Volltext
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