Fuzzy c-means clustering based on the particle swam optimization and immune clone

A new fuzzy cluster algorithm is proposed which takes the advantages of the particle swarm optimization with global searching and fast convergence and immune clonal selection algorithm with diversity antibodies. The new algorithm changes the diversity about particle swarm optimization through clone selection, and particle swarm optimization replaces the fuzzy C-mean iterative process based on the gradient descent method, which makes the new algorithm possess strong searching ability, avoids the fuzzy C-mean algorithm falling into local optimums and reduces the sensitivity to the initials of the fuzzy C-mean algorithm. A real application in classifying two data sets Wine and Iris in machine learning database is provided, Wine has 13 inputs, 3 classes and 178 data vectors. Iris has 4 inputs, 3 classes and 150 data vectors. The accuracy rate has been improved by the hybrid algorithm in comparison with FCM. Experiment results verify that global searching ability is strengthened by Fuzzy C-means clustering based on the particle swarm optimization and immune clone, efficiency and effectiveness of the presented algorithm is also improved.