What's spatial about spatial data mining: Three case studies

被引:0
作者
Shekhar, S [1 ]
Huang, Y [1 ]
Wu, WL [1 ]
Lu, CT [1 ]
Chawla, S [1 ]
机构
[1] Univ Minnesota, Dept Comp Sci, Minneapolis, MN 55455 USA
来源
DATA MINING FOR SCIENTIFIC AND ENGINEERING APPLICATIONS | 2001年 / 2卷
关键词
spatial data mining; feature selection; spatial databases; co-location rules; spatial autocorrelation; spatial outliers;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful, patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. A popular approach is to apply classical data mining techniques after transforming spatial components into non-spatial components via feature selection. An alternative is to explore new models, new objective functions, and new patterns which are more suitable for spatial data and their unique properties. This chapter investigates techniques in the literature to incorporate spatial components via feature selection, new models, new objective functions, and new patterns.
引用
收藏
页码:487 / 514
页数:28
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