An Effective Clustering Method Using a Discrete Particle Swarm Optimization Algorithm-Based Hybrid Approach
Jinghua Guan, Dayou Liu, Haiyang Jia, Peng Yu · 2006
The purpose of this paper is to present and evaluate an improved Naive Bayes algorithm for clustering. Many researchers search for parameter values using EM algorithm. It is well-known that EM approach has a drawback - local optimal solution, so we propose a novel hybrid algorithm of the discrete particle swarm optimization (DPSO) and the EM approach to improve the global search performance. We evaluate this hybrid approach on 4 real-world data sets from UCI repository. In a number of experiments and comparisons, the hybrid DPSO+EM algorithm exhibits a more effective and outperforms the EM approach