<scp>DBKIFCM–PSO</scp> : A Hybrid Approach for Optimized Clustering in Noisy and High‐Dimensional Data

ABSTRACT Fuzzy clustering algorithms have been widely used for complex data patterns and image segmentation using fuzzy partitioning. However, their performance degrades in the presence of noise and higher‐dimensional spaces. To overcome these limitations, we propose a hybrid algorithm, DBKIFCM–PSO, which combines distance‐based kernelized intuitionistic fuzzy C means clustering with particle swarm optimization. This hybrid approach addresses challenges in noisy datasets and higher‐dimensional spaces, providing optimal solutions. We compare DBKIFCM–PSO with existing algorithms on various datasets and demonstrate its superior performance. By integrating DBKIFCM and PSO, our algorithm has shown improved performance on evaluation metrics like , , and over counterpart method by obtaining best values of 0.9595, 0.1036, and −61 120, respectively, on these metrics making it suitable for real‐world applications.