Some of the well-known fuzzy clustering algorithms are based on Euclidean
distance function, which can only be used to detect spherical structural clusters. GustafsonKessel
(GK) clustering algorithm and Gath-Geva (GG) clustering algorithm were developed
to detect non-spherical structural clusters. Both of GG and GK algorithms suffer
from the singularity problem of covariance matrix and the effect of initial status. In
this paper, a new Fuzzy C-Means algorithm based on Particle Swarm Optimization and
Mahalanobis distance without prior information (PSO-FCM-M) is proposed to improve
those limitations of GG and GK algorithms. And we point out that the PSO-FCM algorithm
is a special case of PSO-FCM-M algorithm. The experimental results of two real
data sets show that the performance of our proposed PSO-FCM-M algorithm is better
than those of the FCM, GG, GK algorithms.
Relation:
International Journal of Innovative Computing Information and Control
