电子学报 ›› 2016, Vol. 44 ›› Issue (10): 2530-2534.DOI: 10.3969/j.issn.0372-2112.2016.10.035

• 学术论文 • 上一篇    下一篇

基于核距离的直觉模糊c均值聚类算法

余晓东, 雷英杰, 宋亚飞, 岳韶华, 申晓勇   

  1. 空军工程大学防空反导学院, 陕西西安 710051
  • 收稿日期:2015-02-04 修回日期:2015-06-08 出版日期:2016-10-25
    • 作者简介:
    • 余晓东,男,1989年出生,江西九江人,空军工程大学博士研究生,主要研究方向为模式识别、智能信息处理等.E-mail:1438894571@qq.com;雷英杰,男,1956年出生,陕西渭南人,空军工程大学教授,博士生导师,主要研究方向为智能信息处理与智能决策.
    • 基金资助:
    • 国家自然科学基金 (No.61272011,No.61309022); 陕西省自然科学青年基金 (No.2013JQ8031)

Intuitionistic Fuzzy c-means Clustering Algorithm Based on Kernelled Distance

YU Xiao-dong, LEI Ying-jie, SONG Ya-fei, YUE Shao-hua, SHEN Xiao-yong   

  1. Air and Missile Defense College, Air Force Engineering University, Xi'an, Shaanxi 710051, China
  • Received:2015-02-04 Revised:2015-06-08 Online:2016-10-25 Published:2016-10-25
    • Supported by:
    • National Natural Science Foundation of China (No.61272011, No.61309022); Youth Fund of Natural Science Foundation of Shaanxi Province (No.2013JQ8031)

摘要:

针对现有直觉模糊c均值聚类算法无法发现非凸聚类结构的缺陷,提出了一种基于核化距离的直觉模糊c均值聚类算法.算法在定义了基于核的直觉模糊欧式距离基础上,通过把聚类样本映射到高维特征空间,使原来没有显现的特征突现出来,从而能够更好地聚类.实验选择一组人工数据集及一组UCI数据集测试了本文算法,并将其与五种经典的聚类算法进行了比较.实验结果充分表明了该算法的有效性及优越性.

关键词: 直觉模糊集, 直觉模糊聚类, 核方法, 无监督学习

Abstract:

The intuitionistic fuzzy c-means clustering algorithm cannot discover the non-convex cluster structure.To alleviate this problem,an intuitionistic fuzzy c-means clustering algorithm based on kernelled distance is proposed.By defining the intuitionistic fuzzy Euclid distance,we map the sample to a high-dimension feature space.So the former features can be reflected thoroughly,which is helpful for clustering.Experiments executed on one artificial data sets and one UCI data sets demonstrate the performance of the proposed method.Compared with the five classical cluster algorithms,our method is of obvious effectiveness and superiority.

Key words: intuitionistic fuzzy set, intuitionistic fuzzy clustering, kernel method, unsupervised learning

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