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

doi:10.3969/j.issn.0372-2112.2016.10.035

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Intuitionistic Fuzzy c-means Clustering Algorithm Based on Kernelled Distance

更新时间：2025-07-16

- Intuitionistic Fuzzy c-means Clustering Algorithm Based on Kernelled Distance
- Acta Electronica Sinica Vol. 44, Issue 10, Pages: 2530-2534(2016)
- 作者机构：
  
  空军工程大学防空反导学院,陕西,西安,710051
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61272011, No.61309022);Youth Fund of Natural Science Foundation of Shaanxi Province (No.2013JQ8031)
- DOI：10.3969/j.issn.0372-2112.2016.10.035
  CLC： TP182TP391
- Published：2016
- 稿件说明：
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YU Xiao-dong, LEI Ying-jie, SONG Ya-fei, et al. Intuitionistic Fuzzy c-means Clustering Algorithm Based on Kernelled Distance[J]. Acta Electronica Sinica, 2016, 44(10): 2530-2534.
DOI：

YU Xiao-dong, LEI Ying-jie, SONG Ya-fei, et al. Intuitionistic Fuzzy c-means Clustering Algorithm Based on Kernelled Distance[J]. Acta Electronica Sinica, 2016, 44(10): 2530-2534. DOI： 10.3969/j.issn.0372-2112.2016.10.035.

摘要

针对现有直觉模糊

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

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.

Abstract

The intuitionistic fuzzy

-means clustering algorithm cannot discover the non-convex cluster structure.To alleviate this problem

an intuitionistic fuzzy

-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.

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