Cluster Analysis by Variance Ratio Criterion and PSOSQP Algorithm

Yudong Zhang, Lenan Wu


In order to solve the cluster analysis problem more efficiently, we presented a new approach based on Particle Swarm Optimization Sequence Quadratic Programming (PSOSQP). First, we created the optimization model using the variance ratio criterion (VRC) as fitness function. Second, PSOSQP was introduced to find the maximal point of the VRC. The experimental dataset contained 400 data of 4 groups with three different levels of overlapping degrees: non-overlapping, partial overlapping, and severely overlapping. We compared the PSOSQP with genetic algorithm (GA) and combinatorial particle swarm optimization (CPSO). Each algorithm was run 20 times. The results showed that PSOSQP could found the largest VRC values among all three algorithms, and meanwhile it cost the least time. It can conclude that PSOSQP is effective and rapid for the cluster analysis problem.


Cluster Analysis; Variance Ratio Criterion; Genetic Algorithm; Particle Swarm Optimization; Sequence Quadratic Programming

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