Clustering with Interval-valued Fuzzy Sets

DOI

10.2991/ifsa-eusflat-15.2015.51How to use a DOI?

Keywords

Clustering, interval-valued fuzzy sets, transitivity

Abstract

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].

Copyright

© 2015, 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/).

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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  - 10.2991/ifsa-eusflat-15.2015.51
ID  - GonzálezdelCampo2015/06
ER  -