A Quantum-Inspired Fuzzy based Evolutionary algorithm for data clustering

In this paper, a Quantum-Inspired Evolutionary Fuzzy C-Means (QIE-FCM) algorithm is proposed. The proposed approach find the true number of clusters and the appropriate value of weighted exponent (m) which is required to be known in advance to perform clustering using Fuzzy C-Means (FCM) algorithm. However, the selection of inappropriate value of m and C may lead the algorithm to converge to the local optima. To address the issue of selecting the appropriate value of m and corresponding value of C. In QIE-FCM, the quantum concept is used in classical computer where m is represented in terms of quantum bits (qubits). The QIE-FCM is based on generations. At each generation (g), quantum gates are used to generate a new value of m. For each generated value of m, FCM algorithm is executed by varying values of C. Then, corresponding to m value appropriate value of C is identified by evaluating local fitness function for generation g. To achieve the global best value of m and C, the global fitness function is evaluated by comparing the local best fitness value in current generation with the best fitness value obtained among all the previous generations. To judge the efficacy of QIE-FCM algorithm, it is compared with two well-known indices and three evolutionary fuzzy based clustering algorithm and their performance is evaluated on four benchmark datasets. Furthermore, the sensitivity of QIE-FCM is also experimentally investigated in this paper.