一种自适应模糊连接点聚类算法

模糊连接点聚类算法（Fuzzy Joint Points， FJP）用最大间隔下降法划分聚类的簇数目，这种确定簇数目的方法具有主观性，不利于算法的应用推广。针对此问题，提出一种基于有效近邻簇指标的自适应FJP聚类算法，通过Kernels-VCN指标来评估聚类的有效性，从而实现最佳簇数目的自适应确定，最后在UCI数据集和人工数据集上验证所提算法的可行性。

An Adaptive Fuzzy Joint Points Clustering Algorithm

 Fuzzy Joint Points (FJP) divides the cluster number of clusters by the maximum interval descent method. This method of determining the number of clusters is subjective and is not conducive to the application of the algorithms. Aiming at this problem, an adaptive FJP clustering algorithm based on effective neighbor cluster index is proposed. The Kernels-VCN index is used to evaluate the effectiveness of clustering, so as to achieve the optimal determination of the optimal number of clusters. Finally, we verify the feasibility of the proposed algorithm on UCI datasets and artificial datasets.