Hybrid Clustering Using Firefly Optimization and Fuzzy C-Means Algorithm

作     者：Krishnamoorthi Murugasamy Kalamani Murugasamy Krishnamoorthi Murugasamy;Kalamani Murugasamy

作者机构：Department of CSE Bannari Amman Institute of Technology Sathyamanaglam India Department of ECE Bannari Amman Institute of Technology Sathyamanaglam India 

出 版 物：《Circuits and Systems》 (电路与系统（英文）)

年 卷 期：2016年第7卷第9期

页      面：2339-2348页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Clustering Optimization K-Means Fuzzy C-Means Firefly Algorithm F-Firefly 

摘      要：Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm.