一种自适应模糊连接点聚类算法

doi:10.3969/j.issn.1006-2475.2019.10.011

计算机与现代化 ›› 2019, Vol. 0 ›› Issue (10): 55-.doi: 10.3969/j.issn.1006-2475.2019.10.011

一种自适应模糊连接点聚类算法

(广西大学计算机与电子信息学院，广西南宁530004)

收稿日期:2019-03-26 出版日期:2019-10-28 发布日期:2019-10-29
作者简介:王保锋(1993-)，男，河南信阳人，硕士研究生,研究方向:数据挖掘,E-mail: 251665685@qq.com; 麻晓璇(1994-),女,广西桂林人,硕士研究生,研究方向:数据挖掘,E-mail：ma404783502@163.com; 李金星(1996-),男,山西忻州人,硕士研究生,研究方向:机器学习,E-mail: lijinxingsx@163.com。
基金资助:
国家自然科学基金资助项目(51667004, 61762009)

An Adaptive Fuzzy Joint Points Clustering Algorithm

(School of Computer and Electronic Information, Guangxi University, Nanning 530004, China)

Received:2019-03-26 Online:2019-10-28 Published:2019-10-29

摘要/Abstract

摘要： 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。

关键词: 模糊连接点聚类, 有效近邻簇指标, 最佳划分水平, 最佳簇数

Abstract: 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.

Key words: fuzzy joint point, effective neighbor cluster index, optimal division level, optimal number of clusters

中图分类号:

TP391

王保锋,麻晓璇,李金星. 一种自适应模糊连接点聚类算法[J]. 计算机与现代化, 2019, 0(10): 55-.

WANG Bao-feng, MA Xiao-xuan, LI Jin-xing. An Adaptive Fuzzy Joint Points Clustering Algorithm[J]. Computer and Modernization, 2019, 0(10): 55-.

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一种自适应模糊连接点聚类算法

An Adaptive Fuzzy Joint Points Clustering Algorithm

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