一个新的填充函数在优化核聚类中的应用

A New Filled Function and the Application in Optimizing Kernel Clustering Problem

摘要: 针对约束优化问题，本文提出一个新的单参数填充函数，并设计相应算法。然后将此填充函数应用于对聚类问题中的权重优化，结合聚类模型的过程给出相应算法步骤，运用 Python 对数据集进行聚类分析并得出权重参数。最后，实验结果表明该填充函数算法的可行性。

Abstract: In this paper, we propose a new one parameter filled function for constrained opti-mization and design the corresponding algorithm. Then this filled function is applied to the weight optimization of clustering problem, the corresponding algorithm steps are given in combination with the process of clustering model, and Python is used to cluster the data set and obtain the weight parameters. Finally, the experimental results show that the filled function algorithm is feasible.