A novel quantum inspired genetic algorithm to initialize cluster centers in fuzzy C-means.

• QEE-FCM is aimed to find the optimal initial cluster centers in Fuzzy C-Means. • QEE-FCM use a fuzzy entropy function to measure the fuzziness of clustering. • QEE-FCM solves the FCM problem of getting stuck in a local optimum. • Unlike other QGA-FCM algorithms it not significantly increases the execution times. • Tests are performed on well-known UCI machine learning classification datasets. We propose a new quantum-inspired genetic algorithm (QGA) aimed to find the optimal initial cluster centers of the Fuzzy C-Means (FCM). The quantum chromosomes of the quantum population are collapsed to conventional chromosomes formed by binary strings encoding the cluster centers. We set as fitness function to minimize a fuzzy entropy function used to measure the fuzziness of clustering. A quantum rotation gate operator is applied in any generation to evolve the quantum population. The best conventional chromosome is used to set the initial cluster centers running FCM. We execute comparative tests of our QGA with other quantum-inspired method proposed in literature in order to find the best initial cluster centers: the results show that our method provides the best trade-off between the accuracy and precision of results and execution times. [ABSTRACT FROM AUTHOR]