Algorithms for clustering data
Algorithms for clustering data
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Approximations for efficient computation in the theory of evidence
Artificial Intelligence
Artificial Intelligence
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
An evidence-theoretic k-NN rule with parameter optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Assessing sensor reliability for multisensor data fusion within the transferable belief model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The modified Dempster-Shafer approach to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Belief Classification Approach Based on Generalized Credal EM
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
DK-BKM: decremental K belief K-modes method
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Ranking-based feature selection method for dynamic belief clustering
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Classification systems based on rough sets under the belief function framework
International Journal of Approximate Reasoning
Distances in evidence theory: Comprehensive survey and generalizations
International Journal of Approximate Reasoning
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Clustering techniques are considered as efficient tools for partitioning data sets in order to get homogeneous clusters of objects. However, the reality is connected to uncertainty by nature, and these standard algorithms of clustering do not deal with this uncertainty pervaded in their parameters. In this paper we develop a clustering method in an uncertain context based on the K-modes method and the belief function theory. This so-called belief K-modes method (BKM) provides a new clustering technique handling uncertainty in the attribute values of objects in both the clusters’ construction task and the classification one.