CECM: Constrained evidential C-means algorithm

Authors:
V. Antoine;B. Quost;M. -H. Masson;T. Denux
Affiliations:
Université de Technologie de Compiègne, Heudiasyc, UMR CNRS 6599, BP 20529, 60205 Compiègne, France;Université de Technologie de Compiègne, Heudiasyc, UMR CNRS 6599, BP 20529, 60205 Compiègne, France;Université de Picardie Jules Verne, IUT de l'Oise, Heudiasyc, UMR CNRS 6599, BP 20529, 60205 Compiègne, France;Université de Technologie de Compiègne, Heudiasyc, UMR CNRS 6599, BP 20529, 60205 Compiègne, France
Venue:
Computational Statistics & Data Analysis
Year:
2012

Citing 18
Cited 7

An extension of Karmarkar projective algorithm for convex quadratic programming

Mathematical Programming: Series A and B
The transferable belief model

Artificial Intelligence
A survey of constrained classification

Computational Statistics & Data Analysis
Constrained K-means Clustering with Background Knowledge

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Model-based Clustering with Soft Balancing

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Clustering interval-valued proximity data using belief functions

Pattern Recognition Letters
Non-redundant data clustering

Knowledge and Information Systems
ECM: An evidential version of the fuzzy c-means algorithm

Pattern Recognition
New modifications and applications of fuzzy C-means methodology

Computational Statistics & Data Analysis
Active semi-supervised fuzzy clustering

Pattern Recognition
Defining belief functions using mathematical morphology -- Application to image fusion under imprecision

International Journal of Approximate Reasoning
RECM: Relational evidential c-means algorithm

Pattern Recognition Letters
Active learning with statistical models

Journal of Artificial Intelligence Research
Practical representations of incomplete probabilistic knowledge

Computational Statistics & Data Analysis
Data analysis with fuzzy clustering methods

Computational Statistics & Data Analysis
Measuring constraint-set utility for partitional clustering algorithms

PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
EVCLUS: evidential clustering of proximity data

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A possibilistic approach to clustering

IEEE Transactions on Fuzzy Systems

Editorial: Special issue on fuzzy sets in statistics

Computational Statistics & Data Analysis
Evidential evolving Gustafson--Kessel algorithm for online data streams partitioning using belief function theory

International Journal of Approximate Reasoning
A new belief-based K-nearest neighbor classification method

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
Dealing with uncertainty and imprecision in image segmentation using belief function theory

Quantified Score

Hi-index	0.03

Visualization

Abstract

In clustering applications, prior knowledge about cluster membership is sometimes available. To integrate such auxiliary information, constraint-based (or semi-supervised) methods have been proposed in the hard or fuzzy clustering frameworks. This approach is extended to evidential clustering, in which the membership of objects to clusters is described by belief functions. A variant of the Evidential C-means (ECM) algorithm taking into account pairwise constraints is proposed. These constraints are translated into the belief function framework and integrated in the cost function. Experiments with synthetic and real data sets demonstrate the interest of the method. In particular, an application to medical image segmentation is presented.