Algorithms for clustering data
Algorithms for clustering data
Artificial Intelligence
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Inner and outer approximation of belief structures using a hierarchical clustering approach
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Voting-Merging: An Ensemble Method for Clustering
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Data Clustering Using Evidence Accumulation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Clustering interval-valued proximity data using belief functions
Pattern Recognition Letters
Ensemble Clustering in Medical Diagnostics
CBMS '04 Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems
Moderate diversity for better cluster ensembles
Information Fusion
On fuzzy cluster validity indices
Fuzzy Sets and Systems
ECM: An evidential version of the fuzzy c-means algorithm
Pattern Recognition
Fuzzy Ensemble Clustering for DNA Microarray Data Analysis
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
International Journal of Intelligent Systems - Decision Sciences: Foundations and Applications
EVCLUS: evidential clustering of proximity data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evidential combination of multiple HMM classifiers for multi-script handwriting recognition
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Ensemble clustering in the belief functions framework
International Journal of Approximate Reasoning
Constructing dynamic frames of discernment in cases of large number of classes
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
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In this paper, belief functions, defined on the lattice of partitions of a set of objects, are investigated as a suitable framework for combining multiple clusterings. We first show how to represent clustering results as masses of evidence allocated to partitions. Then a consensus belief function is obtained using a suitable combination rule. Tools for synthesizing the results are also proposed. The approach is illustrated using two data sets.