Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Using fuzzy data mining to evaluate survey data from olive grove cultivation
Computers and Electronics in Agriculture
A method for deriving order compatible fuzzy relations from convex fuzzy partitions
Fuzzy Sets and Systems
| Hi-index | 0.14 |
The issue of validity in clustering is considered and a definition of fuzzy r-cluster that extends E. Ruspini's definition (1982) is proposed. This definition is based on an indistinguishability relation based on the concept of t-norm. The fuzzy r-cluster's metrical properties are studied through the dual concept of t-conorm that leads to G-pseudometrics. From the concept of G-pseudometric, fuzzy r-clusters and fuzzy cluster coverages are defined. The authors propose a measure of cluster validity based on the concept of fuzzy coverage. The basic idea of the approach presented is that the smaller the difference between the degrees of membership and the degrees of indistinguishability, the better the clustering.