Quality Assessment Based on Particle Swarm and Normal Similarity

To assess quality fast and accurate, analyze the K-means clustering, point out that the main advantages of k-means algorithm are its simplicity and speed which allows it to run on large datasets .Introduce the method of particle swarm optimization, through calculation, point out that all the particles are likely to faster convergence on the optimal solution. According to the character of quality assessment that mean and standard deviation are considered, supply a normal similarity method; Result: The method that combines particle swarm optimization with normal similarity to assess quality is feasible.