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

作者：

Highlights：

• 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.

摘要

•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.

论文关键词：QGA,FCM,Quantum population,Quantum chromosome,Quantum evolution,Fuzzy entropy,Fuzziness

论文评审过程：Received 25 December 2020, Revised 9 October 2021, Accepted 27 November 2021, Available online 6 December 2021, Version of Record 9 December 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116340