CEVCLUS: evidential clustering with instance-level constraints for relational data

Recent advances in clustering consider incorporating background knowledge in the partitioning algorithm, using, e.g., pairwise constraints between objects. As a matter of fact, prior information, when available, often makes it possible to better retrieve meaningful clusters in data. Here, this appro...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 18; no. 7; pp. 1321 - 1335
Main Authors: Antoine, V., Quost, B., Masson, M.-H., Denoeux, T.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2014
Springer Nature B.V
Springer Verlag
Subjects:
Algorithms
Artificial Intelligence
Clustering
Computational Intelligence
Computer Science
Constraints
Control
Engineering
Knowledge
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Partitioning
Probability distribution
Robotics
Pairwise constraints
Belief functions
Dempster–Shafer theory
Evidence theory
Relational data
Constrained clustering
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary:Recent advances in clustering consider incorporating background knowledge in the partitioning algorithm, using, e.g., pairwise constraints between objects. As a matter of fact, prior information, when available, often makes it possible to better retrieve meaningful clusters in data. Here, this approach is investigated in the framework of belief functions, which allows us to handle the imprecision and the uncertainty of the clustering process. In this context, the EVCLUS algorithm was proposed for partitioning objects described by a dissimilarity matrix. It is extended here so as to take pairwise constraints into account, by adding a term to its objective function. This term corresponds to a penalty term that expresses pairwise constraints in the belief function framework. Various synthetic and real datasets are considered to demonstrate the interest of the proposed method, called CEVCLUS, and two applications are presented. The performances of CEVCLUS are also compared to those of other constrained clustering algorithms.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-013-1146-z