Fuzzy Clustering Algorithm for Automatic Identification of Clusters

CHEN Hai-peng; SHEN Xuan-jing; LONG Jian-wu; L

doi:10.3969/j.issn.0372-2112.2017.03.028

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Fuzzy Clustering Algorithm for Automatic Identification of Clusters

更新时间：2025-07-16

- Fuzzy Clustering Algorithm for Automatic Identification of Clusters
- Acta Electronica Sinica Vol. 45, Issue 3, Pages: 687-694(2017)
- 作者机构：
  
  1. 吉林大学计算机科学与技术学院,吉林,长春,130012
  2. 吉林大学符号计算与知识工程教育部重点实验室,吉林,长春,130012
  3. 重庆理工大学计算机科学与工程学院,重庆,400054
  4. 吉林大学公共计算机教学与研究中心,吉林,长春,130012
  5. 吉林大学计算机科学与技术学院,吉林,长春,130012
  6. 吉林大学符号计算与知识工程教育部重点实验室,吉林,长春,130012
  7. 重庆理工大学计算机科学与工程学院,重庆,400054
  8. 吉林大学公共计算机教学与研究中心,吉林,长春,130012
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61305046, No.61502065);Natural Science Foundation of Jilin Province (No.20140101193JC, No.20130522117JH, 20150101055JC);Chongqing Research Program of Basic and Frontier Technology (No.cstc2015jcyjBX0127)
- DOI：10.3969/j.issn.0372-2112.2017.03.028
  CLC： TP391
- Published：2017
- 稿件说明：
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CHEN Hai-peng, SHEN Xuan-jing, LONG Jian-wu, et al. Fuzzy Clustering Algorithm for Automatic Identification of Clusters[J]. Acta Electronica Sinica, 2017, 45(3): 687-694.
DOI：

CHEN Hai-peng, SHEN Xuan-jing, LONG Jian-wu, et al. Fuzzy Clustering Algorithm for Automatic Identification of Clusters[J]. Acta Electronica Sinica, 2017, 45(3): 687-694. DOI： 10.3969/j.issn.0372-2112.2017.03.028.

摘要

本文通过集成多次FCM（Fuzzy C-Means）聚类结果以及采用软化分方式，提出一种新的自动确定聚类个数的模糊聚类算法.本算法首先利用不同的聚类数目对数据进行FCM聚类，然后充分利用多次FCM聚类得到的隶属度信息构建一个累积邻接矩阵，最后采用迭代方式对累积邻接矩阵进行图切分以获取最终聚类结果.大量的仿真实验表明，相对现有集成聚类方法，本文方法能够有效减少FCM的聚类次数，并且在图切分过程中的迭代次数为现有方法的1/2左右.

Abstract

To automatically determine the number of clusters

a new fuzzy clustering algorithm is proposed in this study

which is based on soft partition scheme and integrates many FCM clustering results.In this method

FCM clustering is implemented on data by the cluster number;then the membership information is used to build a cumulative adjacency matrix;finally

the graph cut method is adopted to the cumulative adjacency matrix by iterative manner to obtain clustering results.Simulation experiments show that

compared to the current integrated clustering method

our method can effectively reduce the number of FCM clustering;furthermore

its iterations in the graph cut process is about 1/2 of the existing method.

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