期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2012
卷号：3
期号：4
页码：191-193
语种：English
出版社：Ayushmaan Technologies
摘要：Spatial data mining is the application of data mining techniques to spatial data. Data mining in general is the search for hidden patterns that may exist in large databases. Spatial data mining is the discovery of interesting the relationship and characteristics that may exist implicitly in spatial databases. Because of the huge amounts (usually, terabytes) of spatial data that may be obtained from satellite images, medical equipments, video cameras, etc. It is costly and often unrealistic for users to examine spatial data in detail. Spatial data mining aims to automate such a knowledge discovery process. Thus, new and efficient methods are needed to discover knowledge large databases. For this purpose, clustering is one of the most valuable methods in spatial data mining. The main advantage of using clustering is that interesting structures or clusters can be found directly from the data without using any prior knowledge. This paper presents an overview of densitybased methods for spatial data clustering.
关键词：Clustering;DBSCAN;Density- based method;Spatial data.
