CEVCLUS: Constrained evidential clustering of proximity data
- Violaine Antoine, Benjamin Quost, Mylène Masson, Thierry Denoeux
- Corresponding Author
- Violaine Antoine
Available Online August 2011.
- https://doi.org/10.2991/eusflat.2011.80How to use a DOI?
- Semi-supervised clustering, pairwise constraints, belief functions, evidence theory, proximity data.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 PB - Atlantis Press SP - 876 EP - 882 UR - https://doi.org/10.2991/eusflat.2011.80 DO - https://doi.org/10.2991/eusflat.2011.80 ID - Antoine2011/08 ER -