期刊名称：Indian Journal of Computer Science and Engineering
印刷版ISSN：2231-3850
电子版ISSN：0976-5166
出版年度：2010
卷号：1
期号：1
页码：8-12
出版社：Engg Journals Publications
摘要：Mining knowledge from large amounts of spatial data is known as spatial data mining. It becomes a highly demanding field because huge amounts of spatial data have been collected in various applications ranging from geo-spatial data to bio-medical knowledge. The amount of spatial data being collected is increasing exponentially. So, it far exceeded human’s ability to analyze. Recently, clustering has been recognized as a primary data mining method for knowledge discovery in spatial database. The development of clustering algorithms has received a lot of attention in the last few years and new clustering algorithms are proposed. DBSCAN is a pioneer density based clustering algorithm. It can find out the clusters of different shapes and sizes from the large amount of data containing noise and outliers. This paper shows the results of analyzing the properties of density based clustering characteristics of three clustering algorithms namely DBSCAN, k-means and SOM using synthetic two dimensional spatial data sets.
