Hybrid Clustering using Firefly Optimization Technique and K-Means Algorithm Krishnamoorthi M*, Kalamani M** *Department of CSE, Bannari Amman Institute of Technology, Sathyamanaglam, Tamil Nadu, India **Department of ECE, Bannari Amman Institute of Technology, Sathyamanaglam, Tamil Nadu, India Online published on 2 August, 2016. Abstract In the past few decades, cluster analysis has contributed a vital role in a diversity of fields in engineering. Clustering and classification are the two basic tasks in data mining to retrieve the useful information from databases. 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. The k-means clustering algorithm suffers from local convergence problem and it is sensitive to initial seed points selection. To overcome the problems in the k-means and to enhance the FA algorithm, the hybrid K-Firefly algorithm is developed in this paper by incorporating K-means operator in the end of each iteration of the FA algorithm. This proposed K-Firefly algorithm combines the goodness of the two algorithms and improves the clustering accuracy. In this research work, the Hybrid K-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets chosen from UCI Machine learning repository. From the experimental results, it is observed that the Hybrid K-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm. Top Keywords Clustering, Optimization, K-Means, Fuzzy C-Means, Firefly Algorithm, K-Firefly. Top |