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计算机工程 ›› 2008, Vol. 34 ›› Issue (3): 91-93. doi: 10.3969/j.issn.1000-3428.2008.03.032

• 软件技术与数据库 • 上一篇    下一篇

基于统计信息的聚类边界模式检测算法

邱保志,张 枫,岳 峰   

  1. (郑州大学信息工程学院,郑州 450052)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-02-05 发布日期:2008-02-05

Boundary Pattern Detection of Clusters Based on Statistics Information

QIU Bao-zhi, ZHANG Feng, YUE Feng   

  1. (School of Information Engineering, Zhengzhou University, Zhengzhou 450052)
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-02-05 Published:2008-02-05

摘要: 为有效地检测聚类的边界点,提出基于统计信息的边界模式检测算法。根据数据对象的k距离统计信息设定邻域半径,再利用对象邻域范围内邻居的k距离统计信息寻找边界点。实验结果表明,该算法可以有效地检测出任意形状、不同大小和不同密度聚类的边界点,并可以消除噪声。

关键词: 边界点, 聚类, 方差

Abstract: This paper proposes an algorithm named boundary pattern detection based on statistics information to detect boundary points of clusters effectively. BOURN sets neighborhood radius based on the k-dist statistics information of the objects, and searches boundary points based on the k-dist statistics information of neighbors in the neighborhood around it. Experimental results show that BOURN can find boundary points of clusters of arbitrary shapes, different sizes and different density, and can remove noise effectively.

Key words: boundary points, clusters, variance

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