计算机科学 ›› 2009, Vol. 36 ›› Issue (3): 220-222.

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基于密度加权的粗糙K-均值聚类改进算法

郑超 苗夺谦 王睿智   

  1. 同济大学计算科学与技术系,上海201804
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受国家自然科学基金项目(60775036,60475019),高等学校博士学科点专项科研基金(20060247039)资助.

ZHENG Chao ,MIAO Duo-qian ,WANG Rui-zhi (Department of Computer Science and Technology, Tongji University, Shanghai 201804,China)   

  • Online:2018-11-16 Published:2018-11-16

摘要: 针对粗糙K-均值聚类算法中类均值计算式的特点,提出了一种改进的粗糙K-均值算法。改进后的算法基于数据对象所在区域的密度,在类的均值计算过程中对每个对象赋以不同的权重。不同测试数据集的实验结果表明,改进后的粗糙10均值算法提高了聚类的准确性,降低了迭代次数,并且可以有效地减小孤立点对聚类的影响。

关键词: 聚类算法 粗糙K-均值 密度 孤立点

Abstract: According to the feature of the calculation of means in Rough K-means algorithm, an improved Rough K- means algorithm was proposed. The new algorithm introduces weights to the calculation of means, which is based on the density of each point. The experime

Key words: Clustering algorithm, Rough K-means, Density, Outlier

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