An optimized genetic K-means clustering algorithm

Traditional K-means algorithm is sensitive to the initial cluster centers, cluster results fluctuate with different initial input and are easy to fall into local optimum. This paper proposes an optimized genetic K-means clustering algorithm based on genetic algorithm. Use encoding, initialization, fitness function selection, crossover and mutation of genetic algorithms into clustering problem. Experiment proves this algorithm has superior performance than the traditional K-means algorithm.