Enhanced Immune Genetic Algorithm-Based Fuzzy Clustering

This article presents an enhanced fuzzy clustering algorithm based on an improved immune genetic approach. The algorithm incorporates innovative mechanisms such as antibody diversity maintenance, affinity maturation, and memory cell preservation to optimize clustering performance. Key implementation features include adaptive mutation rates, antibody concentration regulation, and fuzzy membership function optimization. The downloadable code package contains complete MATLAB/Python implementation with configurable parameters for cluster validation indices, genetic operator customization, and fuzzy C-means integration. Through this resource, researchers can explore the algorithm's detailed mechanics, including population initialization, affinity calculation, and cluster center optimization processes. The implementation demonstrates practical applications in pattern recognition and data mining with comprehensive documentation on parameter tuning and performance evaluation metrics.