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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Bibliographic Details
Published in:Knowledge-based systems Vol. 142; pp. 29 - 44
Main Authors: Li, Feng, Li, Shoumei, Denœux, Thierry
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 15.02.2018
Elsevier Science Ltd
Elsevier
Subjects:
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 Access:Get full text
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Summary: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. CEVCLUS is a version of EVCLUS allowing one to use prior information on cluster membership, in the form of pairwise must-link and cannot-link constraints. The original CEVCLUS algorithm was shown to have very good performances, but it was quite slow and limited to small datasets. In this paper, we introduce a much faster and efficient version of CEVCLUS, called k-CEVCLUS, which is both several orders of magnitude faster than EVCLUS and has storage and computational complexity linear in the number of objects, making it applicable to large datasets (around 104 objects). We also propose a new constraint expansion strategy, yielding drastic improvements in clustering results when only a few constraints are given.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2017.11.023