A powerful hybrid clustering method based on modified stem cells and Fuzzy C-means algorithms

• We modified the stem cells algorithm (SCA) and used FCM for data clustering.• This algorithm, not only helps the FCM clustering escape from local optima.• This algorithm overcomes the shortcoming of the slow convergence speed of the SCA.• The performance of SC-FCM is compared with the six clustering methods on six datasets.• Experiment results indicate the superiority of the SC-FCM algorithm.

One of the simple techniques for Data Clustering is based on Fuzzy C-means (FCM) clustering which describes the belongingness of each data to a cluster by a fuzzy membership function instead of a crisp value. However, the results of fuzzy clustering depend highly on the initial state selection and there is also a high risk for getting the best results when the datasets are large. In this paper, we present a hybrid algorithm based on FCM and modified stem cells algorithms, we called it SC-FCM algorithm, for optimum clustering of a dataset into K clusters. The experimental results obtained by using the new algorithm on different well-known datasets compared with those obtained by K-means algorithm, FCM, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) Algorithm demonstrate the better performance of the new algorithm.