期刊名称：International Journal of Computer Science & Information Technology (IJCSIT)
印刷版ISSN：0975-4660
电子版ISSN：0975-3826
出版年度：2012
卷号：4
期号：4
页码：71
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Data mining, which is refers to as Knowledge Discovery in Databases(KDD), means a process of nontrivialexaction of implicit, previously useful and unknown information such as knowledge rules, descriptions,regularities, and major trends from large databases. Data mining is evolved in a multidisciplinary field ,including database technology, machine learning, artificial intelligence, neural network, informationretrieval, and so on. In principle data mining should be applicable to the different kind of data and databasesused in many different applications, including relational databases, transactional databases, datawarehouses, object- oriented databases, and special application- oriented databases such as spatialdatabases, temporal databases, multimedia databases, and time- series databases. Spatial data mining, alsocalled spatial mining, is data mining as applied to the spatial data or spatial databases. Spatial data are thedata that have spatial or location component, and they show the information, which is more complex thanclassical data. A spatial database stores spatial data represents by spatial data types and spatialrelationships and among data. Spatial data mining encompasses various tasks. These include spatialclassification, spatial association rule mining, spatial clustering, characteristic rules, discriminant rules,trend detection. This paper presents how spatial data mining is achieved using clustering.
关键词：Clustering; Database; Data mining; Spatial data.
