A Hybrid Quantum-Behaved Particle Swarm Optimization Algorithm for Clustering Analysis
Kezhong Lu, Kangnian Fang, Guangqian Xie · 2008
The K-harmonic means (KHM) is a center-based clustering algorithm which uses the harmonic averages of the distances from each data point to the centers as components to its performance function. Unlike K-means, KHM is less sensitive to initial conditions. However, KHM as a center-based clustering algorithm can only generate a local optimal solution. In this paper, we present a hybrid clustering algorithm combining quantum-behaved particle swarm optimization and K-harmonic means (HQPSO) for solving this problem. This algorithm has been implemented and tested on several simulated and real datasets. The performance of this algorithm is compared with KHM, PSO, HPSO and QPSO. Our computational simulations reveal the HQPSO clustering algorithm has the advantage of global searching, fast convergence and less sensitive to initial conditions. The HQPSO is a robust clustering algorithm.