CEVCLUS: Constrained evidential clustering of proximity data

DOI

10.2991/eusflat.2011.80How to use a DOI?

Keywords

Semi-supervised clustering, pairwise constraints, belief functions, evidence theory, proximity data.

Abstract

We present an improved relational clustering method integrating prior information. This new algorithm, entitled CEVCLUS, is based on two concepts: evidential clustering and constraint-based clustering. Evidential clustering uses the DempsterShafer theory to assign a mass function to each object. It provides a credal partition, which subsumes the notions of crisp, fuzzy and possibilistic partitions. Constraint-based clustering consists in taking advantage of prior information. Such background knowledge is integrated as an additional term in the cost function. Experiments conducted on synthetic and real data demonstrate the interest of the method, even for unbalanced datasets or non-spherical classes.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Violaine Antoine
AU  - Benjamin Quost
AU  - Mylène Masson
AU  - Thierry Denoeux
PY  - 2011/08
DA  - 2011/08
TI  - CEVCLUS: Constrained evidential clustering of proximity data
BT  - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11)
PB  - Atlantis Press
SP  - 876
EP  - 882
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.80
DO  - 10.2991/eusflat.2011.80
ID  - Antoine2011/08
ER  -