Spatial Data Mining
Spatial Data Mining
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DOI：
10.1007/978-0-387-09823-4_43
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Shashi Shekhar;Pusheng Zhang;Yan Huang
中科院分区：
文献类型：
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作者：
Shashi Shekhar;Pusheng Zhang;Yan Huang
空间数据挖掘是从大型空间数据集中发现有趣的、以前未知的、但可能有用的模式的过程。由于空间数据类型、空间关系和空间自相关性的复杂性，从空间数据集中提取有趣和有用的模式比从传统的数值和分类数据中提取相应的模式要困难得多。本章概述了空间数据挖掘区别于经典数据挖掘的独特特征，并介绍了空间数据挖掘研究的主要成就。
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. This chapter provides an overview on the unique features that distinguish spatial data mining from classical Data Mining, and presents major accomplishments of spatial Data Mining research.