An efficient Meta-Cognitive Fuzzy C-Means clustering approach

Abstract This paper presents an efficient Meta-Cognitive Fuzzy C-Means (McFCM) clustering approach inspired by human meta-cognitive learning principles. Unlike other clustering approaches that use all the samples for learning, McFCM adopts a human like cognitive learning strategy to select the samples for learning, resulting in the use of lesser samples and faster convergence. McFCM consists of two units namely, a cognitive and a meta-cognitive unit. The cognitive unit is the basic fuzzy clustering unit which clusters the input data. The meta-cognitive unit consists of a replica of the cognitive unit along with performance measures for monitoring and learning strategies used for control actions. The meta-cognitive unit controls the learning process in the cognitive unit by deciding on what-to-learn, when-to-learn and how-to-learn. Detailed performance evaluation of McFCM is presented using seven benchmark classification problems from the UCI repository and the results have been statistically compared with other well known existing clustering methods. The results show a superior performance of McFCM. Performance of McFCM is also presented using a real world Autism Spectrum Disorder Detection problem with better classification accuracies compared to other existing methods.