TITLE:
Hybrid Clustering Using Firefly Optimization and Fuzzy C-Means Algorithm
AUTHORS:
Krishnamoorthi Murugasamy, Kalamani Murugasamy
KEYWORDS:
Clustering, Optimization, K-Means, Fuzzy C-Means, Firefly Algorithm, F-Firefly
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.9,
July
19,
2016
ABSTRACT: 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.