Clustering with Interval-valued Fuzzy Sets
Ramón González del Campo, Jose Luis González
Ramón González del Campo
Available Online June 2015.
- https://doi.org/10.2991/ifsa-eusflat-15.2015.51How to use a DOI?
- Clustering, interval-valued fuzzy sets, transitivity
- Interval-Valued Fuzzy Sets handle uncertainty and vagueness effectively. These features are particularly useful for clustering. In this paper it is showed the utility of Interval-Valued Fuzzy Sets for clustering with no accurate information. An easy method for clustering is proposed by generating transitive closures under a pseudo-t-representable t-norm. Clusters are computed from transitive closures by generating alpha-cuts. It is found that some of these alpha-cuts are equivalence classes under the pseudo-t-representable min. It is also found that these transitive closures are closer to the original interval-valued fuzzy relation that the classical transitive closure under the t-norm [min, min].
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Ramón González del Campo AU - Jose Luis González PY - 2015/06 DA - 2015/06 TI - Clustering with Interval-valued Fuzzy Sets BT - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 351 EP - 356 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.51 DO - https://doi.org/10.2991/ifsa-eusflat-15.2015.51 ID - GonzálezdelCampo2015/06 ER -