计算机工程 ›› 2011, Vol. 37 ›› Issue (14): 282-284.doi: 10.3969/j.issn.1000-3428.2011.14.096

• 开发研究与设计技术 • 上一篇    下一篇

基于空间局部偏离因子的离群点检测算法

张天佑,王小玲   

  1. (中南大学信息科学与工程学院,长沙 410083)
  • 收稿日期:2011-02-16 出版日期:2011-07-20 发布日期:2011-07-20
  • 作者简介:张天佑(1983-),男,硕士研究生,主研方向:数据挖掘;王小玲,教授
  • 基金项目:
    国家自然科学基金资助项目(60773013)

Outlier Detection Algorithm Based on Space Local Deviation Factor

ZHANG Tian-you, WANG Xiao-ling   

  1. (College of Information Science and Engineering, Central South University, Changsha 410083, China)
  • Received:2011-02-16 Online:2011-07-20 Published:2011-07-20

摘要: 针对空间数据集的特性,提出一种基于空间局部偏离因子(SLDF)的离群点检测算法。利用SLDF度量空间点对象的离群程度,计算空间数据集中点对象的SLDF值并对其进行排序,将取值较大的前M个点对象作为空间离群点。实验结果表明,该算法能较好地检测空间局部离群点,其有效性与准确性均优于SLZ算法,适用于高维大数据集的空间离群点检测。

关键词: 属性权向量, 空间离群点, 空间对象距离, 空间局部偏离因子

Abstract: According to the characteristics of spatial data sets, this paper proposes an outlier detection algorithm based on the Space Local Deviation Factor(SLDF). The algorithm uses SLDF to measure the deviate degree of space points object. It calculates all the points’ SLDF, sorts by their values, and uses the top M as the space outlier. Experimental result shows that the algorithm can well detect space outlier and be more applicable to the high dimensional and large data sets, its validity and accuracy of the algorithm are superior to that of SLZ algorithm.

Key words: attribute weighted vector, space outlier, space object distance, Space Local Deviation Factor(SLDF)

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