A New Hybrid Genetic K-means Clustering algorithm

摘要

Recently, intelligent algorithm based K-Means clustering algorithm proposed by many researchers for the respective advantages of clustering algorithm and computational intelligence. This paper present a genetic K-Means algorithm combined with simulated annealing. Our method hinges on the use of simulated annealing to perform a density estimation of the data at various locations for algorithm initialisation, and it's easy to change parameters in real time while generating various start points.Theoretical analysis and experimental results show that our hybrid algorithm has better clustering quality and general performance.