一种邻域粒K均值聚类方法
作者:
作者单位:

1.厦门理工学院;2.易成功(厦门)信息科技有限公司

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中图分类号:

TP181

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


A neighborhood granular K-means clustering method
Author:
Affiliation:

XMUT

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    K均值聚类属于无监督学习,具有简单、易用的特点,是一种广泛使用的聚类分析方法.然而,对于非凸、稀疏及模糊的非线性可分数据,其聚类效果不佳.通过引入粒计算理论,采用邻域粒化技术,提出了一种邻域粒K均值聚类方法.样本在单特征上使用邻域粒化技术构造邻域粒子,在多特征上使用邻域粒化技术形成邻域粒向量.通过定义邻域粒与邻域粒向量的大小、度量和运算规则,提出两种邻域粒距离度量,并对所提出的邻域粒距离度量进行了公理化证明.最后,采用多个UCI数据集进行实验,将K均值聚类算法分别结合两种邻域粒距离度量,在邻域参数和距离度量两个方面与经典聚类算法进行了比较,其结果表明了所提出的邻域粒K均值聚类方法的可行性和有效性.

    Abstract:

    K-means clustering belongs to unsupervised learning and is simple and easy to use. It is a widely used clustering analysis method. However, for non-convex, sparse and fuzzy nonlinear separable data, the clustering effect is not good. By introducing granule computing theory and using neighborhood granulation technology, a neighborhood granule K-means clustering method is proposed. The sample uses neighborhood granulation technology to construct neighborhood granules on a single feature, and to form neighborhood granule vectors on multiple features. By defining the size, measurement and operation rules of neighborhood granules and neighborhood granule vectors, two kinds of neighborhood granule distance measurement are proposed, and the axiomatic proof of the proposed neighborhood granule distance measurement is carried out. Finally, several UCI data sets are used to carry out experiments, the K-means clustering algorithm is combined with two neighborhood granule distance measurements respectively. It is compared with the classical clustering algorithm in two aspects of neighborhood parameters and distance measurement. The results show that the proposed neighborhood granular K-means clustering method is feasible and effective.

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历史
  • 收稿日期:2021-09-04
  • 最后修改日期:2021-12-10
  • 录用日期:2021-12-30
  • 在线发布日期: 2022-02-01
  • 出版日期: