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.