Algorithms for clustering data
Algorithms for clustering data
Combining belief functions when evidence conflicts
Decision Support Systems
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
The transferable belief model and other interpretations of Dempster-Shafer's model
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
K-anonymization incremental maintenance and optimization techniques
Proceedings of the 2007 ACM symposium on Applied computing
IK-BKM: An incremental clustering approach based on intra-cluster distance
AICCSA '10 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010
DK-BKM: decremental K belief K-modes method
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Clustering approach using belief function theory
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
EVCLUS: evidential clustering of proximity data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, we investigate the problem of dynamic belief clustering. The developed approach tackles the problem of updating the partition by decreasing the attribute set in an uncertain context. We propose a based-ranking feature selection method that allows us to preserve only the most relevant attributes. We deal with uncertainty related to attribute values, which is represented and managed through the Transferable Belief Model (TBM) concepts. The reported results showed that, in general, there is a beneficial effect of using the developed selection method to cluster dynamic feature set in comparison with the other static methods performing a complete reclustering.