TY  - JOUR
T1  - Generalized weighted conditional fuzzy clustering.
JF  - IEEE Transactions on fuzzy systems
A1  - Leski, J.M
LA  - English
UL  - https://tuklas.up.edu.ph/Record/UP-99796217609532494
AB  - Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. Among many existing modifications of this method, conditional or context-dependent c-means is the most interesting one. In this method, data vectors are clustered under conditions based on linguistic terms represented by fuzzy sets. This paper introduces a family of generalized weighted conditional fuzzy c-means clustering algorithms. This family include both the well-known fuzzy c-means method and the conditional fuzzy c-means method. Performance of the new clustering algorithm is experimentally compared with fuzzy c-means using synthetic data with outliers and the Box-Jenkins database.
KW  - Box-Jenkins database.
KW  - Clustering algorithm.
KW  - Conditional fuzzy c-means method.
KW  - Generalized weighted conditional fuzzy clustering.
KW  - Natural vague boundaries.
KW  - Outliers.
ER  -