Cluster Analysis by Variance Ratio Criterion and Firefly Algorithm
Yudong Zhang, Dayong Li · International Journal of Digital Content Technology and its Applications · 2013
In order to solve the cluster analysis problem more efficiently, we presented a new approach based on firefly algorithm (FA). First, we created the optimization model using the variance ratio criterion (VRC) as fitness function. Second, FA was introduced to find the maximal point of the VRC. The experimental dataset contains 400 data of 4 groups with three different levels of overlapping degrees: non-overlapping, partial overlapping, and severely overlapping. We compared the FA with genetic algorithm (GA) and combinatorial particle swarm optimization (CPSO). Each algorithm was run 20 times. The results show that FA can found the largest VRC values among all three algorithms, while costs the least time. Therefore, FA is effective and rapid for the cluster analysis problem.