计算机应用 ›› 2014, Vol. 34 ›› Issue (7): 1997-2000.DOI: 10.11772/j.issn.1001-9081.2014.07.1997

• 数据技术 • 上一篇    下一篇

基于改进粒计算的K-medoids聚类算法

潘楚,罗可   

  1. 长沙理工大学 计算机与通信工程学院,长沙 410114
  • 收稿日期:2013-12-30 修回日期:2014-02-04 出版日期:2014-07-01 发布日期:2014-08-01
  • 通讯作者: 潘楚
  • 作者简介:潘楚(1986-),男,湖南怀化人,硕士研究生,主要研究方向:数据挖掘;罗可(1961-),男,湖南长沙人,教授,博士,主要研究方向:数据挖掘。
  • 基金资助:

    国家自然科学基金资助项目;湖南省自然科学衡阳联合基金;湖南省科技计划项目

Improved K-medoids clustering algorithm based on improved granular computing

PAN Chu,LUO Ke   

  1. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha Hunan 410114, China
  • Received:2013-12-30 Revised:2014-02-04 Online:2014-07-01 Published:2014-08-01
  • Contact: PAN Chu

摘要:

针对传统K-medoids聚类算法对初始聚类中心敏感、收敛速度缓慢以及聚类精度不够高等缺点,提出一种基于改进粒计算、粒度迭代搜索策略和优化适应度函数的新算法。该算法利用粒计算思想在有效粒子中选择K个密度大且距离较远的粒子,选择其中心点作为K个聚类初始中心点;并在对应的K个有效粒子中进行中心点更新,来减少迭代次数;采用类间距离和类内距离优化适应度函数来提高聚类的精度。实验结果表明:该算法在UCI多个标准数据集中测试,在有效缩短迭代次数的同时提高了算法聚类准确率。

Abstract:

Due to the disadvantages such as sensitive to the initial selection of the center, slow convergent speed and poor accuracy in traditional K-medoids clustering algorithm, a novel K-medoids algorithm based on improved Granular Computing (GrC), granule iterative search strategy and a new fitness function was proposed in this paper. The algorithm selected K granules using the granular computing thinking in the high-density area which were far apart, selected its center point as the K initial cluster centers, and updated K center points in candidate granules to reduce the number of iterations. What's more, a new fitness function was presented based on between-class distance and within-class distance to improve clustering accuracy. Tested on a number of standard data sets in UCI, the experimental results show that this new algorithm reuduces the number of iterations effectively and improves the accuracy of clustering.

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