The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization and detection of noise in clustering
Pattern Recognition Letters
Artificial Intelligence
Classification on pairwise proximity data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering interval-valued proximity data using belief functions
Pattern Recognition Letters
Feature Discovery in Non-Metric Pairwise Data
The Journal of Machine Learning Research
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise classifier combination using belief functions
Pattern Recognition Letters
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
ECM: An evidential version of the fuzzy c-means algorithm
Pattern Recognition
Fuzzy c-means clustering with prior biological knowledge
Journal of Biomedical Informatics
Decision making in the TBM: the necessity of the pignistic transformation
International Journal of Approximate Reasoning
Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
DK-BKM: decremental K belief K-modes method
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Classifier fusion in the Dempster--Shafer framework using optimized t-norm based combination rules
International Journal of Approximate Reasoning
CECM: Constrained evidential C-means algorithm
Computational Statistics & Data Analysis
Clustering with proximity knowledge and relational knowledge
Pattern Recognition
Robust kernelized approach to clustering by incorporating new distance measure
Engineering Applications of Artificial Intelligence
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
International Journal of Approximate Reasoning
An extension to Rough c-means clustering based on decision-theoretic Rough Sets model
International Journal of Approximate Reasoning
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A new clustering algorithm for proximity data, called RECM (relational evidential c-means) is presented. This algorithm generates a credal partition, a new clustering structure based on the theory of belief functions, which extends the existing concepts of hard, fuzzy and possibilistic partitions. Two algorithms, EVCLUS (Evidential Clustering) and ECM (evidential c-means) were previously available to derive credal partitions from data. EVCLUS was designed to handle proximity data, whereas ECM is a direct extension of fuzzy clustering algorithms for vectorial data. In this article, the relational version of ECM is introduced. It is compared to EVCLUS using various datasets. It is shown that RECM provides similar results to those given by EVCLUS. However, the optimization procedure of RECM, based on an alternate minimization scheme, is computationally much more efficient than the gradient-based procedure used in EVCLUS.