Abstract: Background: Clustering analysis has gained popularity and imprecise methods or their hybrid approaches has attracted many researchers of late. Fuzzy C-means clustering algorithm (FCM) is a method that is frequently used in pattern recognition. Recently, intuitionistic Fuzzy C-means (IFCM) algorithm was introduced and studied by Tripathy and it was found to be superior to all other algorithms in this family. Materials and Methods: This study proposes a modified IFCM method called kernel-based intuitionistic fuzzy C-means (mKIFCM) which is an extension of intuitionistic fuzzy C-means by adopting a kernel induced metric in the data space to replace the original Euclidean norm metric. The mKIFCM method combines Atanassov’s Intuitionistic Fuzzy Entropy (IFE) with kernel-based fuzzy C-means and DNA genetic algorithms (DNA-GA) are optimally used simultaneously to choose the parameters of mKIFCM. The entire algorithm procedure is called mKIFCM-DNAGA. Results: The mKIFCM can make use of the advantages of intuitionistic fuzzy sets, kernel functions and DNA-GA in actual clustering problems. Conclusion: The algorithm is evaluated through cluster validity measures. The clustering accuracy of algorithm is investigated by classification datasets with labeled patterns. Experiments on machine learning repository datasets show that the proposed mKIFCM-DNAGA is more efficient than conventional algorithms. The mKIFCM-DNAGA method maintains appreciable performance compared to other methods in terms of pureness ratio.