Clustering Ensemble Algorithm Based on New Members Selection Method

FENG Zhong-hui; HE Liang; WANG Dong

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Clustering Ensemble Algorithm Based on New Members Selection Method

更新时间：2026-09-04

- Clustering Ensemble Algorithm Based on New Members Selection Method
- Vol. 33, Issue 11, Pages: 25-29(2016)
- 作者机构：
  
  1. 西安交通大学电子与信息工程学院,陕西,西安,710049
  2. 洋浦经济开发区经济发展局,海南,海口,578001
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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冯中慧, 何亮, 王栋. 基于新的成员选择方法的聚类融合算法[J]. 微电子学与计算机, 2016, 33(11): 25-29.

FENG Zhong-hui, HE Liang, WANG Dong. Clustering Ensemble Algorithm Based on New Members Selection Method[J]. 2016, 33(11): 25-29.
冯中慧, 何亮, 王栋. 基于新的成员选择方法的聚类融合算法[J]. 微电子学与计算机, 2016, 33(11): 25-29. DOI：

FENG Zhong-hui, HE Liang, WANG Dong. Clustering Ensemble Algorithm Based on New Members Selection Method[J]. 2016, 33(11): 25-29. DOI：

摘要

聚类融合是聚类分析领域的一个研究热点

它将针对同一问题产生的多个聚类成员(即聚类结果)融合成一个结果

以提高聚类算法的鲁棒性和准确性.在聚类融合过程中

聚类成员的"质量"非常重要

一些"质量"比较差的聚类成员会直接影响聚类融合的结果.在深入研究聚类融合算法基础上

提出新的加权JP(Jaccard index-Precision)聚类成员选择方法和基于该方法的聚类融合算法.在多个不同数据集上的实验结果表明

利用这种新的聚类成员选择方法可以有效地改善聚类融合算法结果的准确性和鲁棒性

性能显著提高.

Abstract

Clustering ensemble is one attractive field of cluster analysis. Focus on improving the robustness and precision of clustering algorithms

clustering ensemble methods combine multiple clustering members (clustering results) of the same issue into one result. During the process of clustering ensemble

the quality of clustering members is very important

some poor clustering members will directly affect the results of clustering ensemble. In this paper

clustering ensemble algorithms are studied

clustering ensemble algorithms based on new clustering members selection method using weighted JP(Jaccard index-Precision) are proposed. Experimental results show that precision and robustness of clustering are both improved on different datasets. The clustering performance is improved significantly.

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references

Levine E, Domany E. Resampling method for unsupervised estimation of cluster validity[J]. Nerural Comput, 2008, 13(11): 2573-2593. DOI: 2573

Beijing Jiawei Han, Micheline Kamber, Jian Pei. Data mining: concept and techniques[M]. 3rd ed. Beijing: China Machine Press, 2012.

Hadjitodorov S T, Kuncheva L I, Todorova L P. Moderate diversity for better cluster ensembles[J]. Information Fusion, 2006, 7(3): 264-275. DOI: 264

Fischer B, Buhmann J M. Path-based clustering for grouping of smooth curves and texture segmentation[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2003, 25(4): 513-518. DOI: 513

Berlin Heidelbarg Constantinos Boulis, Mari Ostendorf. Combining multiple clustering systems[M]. Berlin Heidelbarg: Springer, 2004: 63-74. DOI: 63

刘丽敏, 樊晓平, 廖志芳. 选择性聚类融合新方法研究[J]. 计算机应用研究, 2012, 29(11): 1-5. DOI: 1

唐伟, 周志华. 基于Bagging的选择性聚类集成[J]. 软件学报, 2005, 16(4): 496-502. DOI: 496

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