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A Novel Ant Colony Optimization Algorithm for Clustering

机译:一种新的蚁群聚类算法

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摘要

Clustering Analysis is one kind of pattern recognition that not to be supervised. The Clustering algorithm based on object function resolves the clustering problem into optimization problem, thereby it becomes to the main investigatory stream nowadays. But it has some shortcomings such as its sensitivity to initial condition, and it is easy to fall in local peak. To overcome these deficiencies, ant colony optimization algorithm is applied to clustering analysis and a novel clustering based on an improved ant colony optimization algorithm is proposed. Theoretical analysis and experiments show this method is faster and more efficient to convergence upon the optimal value in the whole field.
机译:聚类分析是一种不受监督的模式识别。基于目标函数的聚类算法将聚类问题解决为优化问题,从而成为当今研究的主流。但是它有一些缺点,例如对初始条件的敏感性,并且很容易落入局部峰。为了克服这些不足,将蚁群优化算法应用于聚类分析,并提出了一种基于改进蚁群优化算法的新型聚类算法。理论分析和实验表明,该方法可以更快,更有效地收敛到整个领域的最优值。

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