CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
ECM: An evidential version of the fuzzy c-means algorithm
Pattern Recognition
Clustering decomposed belief functions using generalized weights of conflict
International Journal of Approximate Reasoning
RECM: Relational evidential c-means algorithm
Pattern Recognition Letters
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
Incremental clustering using a core-based approach
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
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
Ranking-based feature selection method for dynamic belief clustering
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
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This paper deals with the dynamic clustering under uncertainty by developing a decrementalKBeliefK-modes method (DK-BKM). Our clustering DK-BKM method tackles the problem of decreasing the number of clusters in an uncertain context using the Transferable Belief Model (TBM). The proposed approach generalizes belief K-modes method (BKM) to a dynamic environment. Thus, this so-called DK-BKM method provides a new clustering technique handling uncertain categorical attribute's values of dataset objects where dynamic clusters' number is considered. Using the dissimilarity measure concept makes us to update the partition without performing complete reclustering. Experimental results of this dynamic approach show good performance on well-known benchmark datasets.