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A new hybrid learning-based algorithm for data clustering

机译:一种新的数据群集混合学习算法

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In this paper a new hybrid algorithm based on particle swarm optimization (PSO), k-means and learning automata (KPSOLA) is proposed for data clustering. In the proposed algorithm, learning automata acts as the thinking brain of the particles in PSO. In each of iterations of the proposed algorithm execution, corresponding learning automata of each particle decides whether next move of that particle to be with respect to PSO algorithm or with respect to k-means algorithm. The proposed algorithm and also 4 other clustering algorithms have been used for clustering 6 standard datasets and their efficiencies are compared with each other. Experimental results show that the proposed algorithm has an acceptable efficiency and robustness.
机译:本文提出了一种基于粒子群优化(PSO),K均值和学习自动机(KPSOLA)的新的混合算法进行数据集群。在所提出的算法中,学习自动机充当PSO中颗粒的思维脑。在所提出的算法执行的每个迭代中,每个颗粒的相应学习自动机决定了该粒子的下一步是否相对于PSO算法或关于K-Means算法。所提出的算法和4种其他聚类算法已经用于聚类6标准数据集,并且它们的效率相互比较。实验结果表明,该算法具有可接受的效率和鲁棒性。

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