Discrete particle swarm optimization and EM hybrid approach for naive bayes clustering

This paper presents an improved Naive Bayes algorithm for clustering. Many researchers search for parameter values from incomplete data using EM (Expectation Maximization) algorithm. It is well-known that EM approach has a drawback – local optimal solution, so we propose a novel hybrid algorithm of the DPSO (Discrete Particle Swarm Optimization) and the EM approach to improve the global search performance. We then apply the approach to 4 real-world data sets from UCI repository and compare the performance of clustering by the new algorithm with by EM algorithm. In the comparison, the hybrid DPSO+EM algorithm exhibits more effectively and outperforms the EM approach.