Quality Assessment Based on Particle Swarm and Normal Similarity

Tie Wang, Gaonan Wang, Zhiguang Chen, Jianyang Lin
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Abstract

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.
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基于粒子群和法向相似度的质量评价
为了快速准确地评估质量,分析了K-means聚类,指出K-means算法的主要优点是简单和速度快,可以在大数据集上运行。介绍了粒子群优化的方法,通过计算,指出所有的粒子都可能更快地收敛到最优解上。根据综合考虑均数和标准差的质量评价特点,提出了一种正态相似法;结果:将粒子群优化与法向相似度相结合的方法进行质量评价是可行的。
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