一种基于多属性的空间例外挖掘改进算法

A spatial outlier detection algorithm based on multi-non-spatial attributes

空间例外是指与其邻域内其它数据表现不一致或者是偏离观测值以至使人们认为是由不同体制产生的观测点.传统的例外挖掘是根据一个非空间属性值进行例外判断,这种方法容易引起判断失误.在对多个属性进行考虑的基础上,提出了一种基于多属性的空间例外挖掘算法,并与属性加权算法在正确性和有效性方面进行了比较分析.实验证明算法可以有效地发现例外数据.

Spatial outliers have been informally defined as observations in a data set which appear to be inconsistent with the remainder of that set of spatial data,or which deviate so much from other observations so as to arouse suspicions that they were generated by a different mechanism.The conventional spatial outliers detections are used to determine based on a non-spatial attribute.It is easy to arise estimation misplay.This paper presents a spatial outlier algorithm based on multi-non-spatial attributes to estimate outliers.Computation time complexity and validity is analyzed and compared with multi-non-spatial attribute-weighted.Experiments demonstrate that our approach can effectively identify local abnormality in large spatial data sets.