Scaling up the DBSCAN Algorithm for Clustering Large Spatial Databases Based on Sampling Technique

查看全文 作　　者：Guan Ji hong 1, Zhou Shui geng 2, Bian Fu ling 3, He Yan xiang 1 1. School of Computer, Wuhan University, Wuhan 430072, China;2.State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China;3.College of Remote Sensin 出　　处：《Wuhan University Journal of Natural Sciences》索引2001年第6卷第Z1期,共7页 基　　金：Supported by the Open Researches Fund Program of L IESMARS(WKL(0 0 ) 0 30 2 ) 摘　　要：Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with DBSCAN algorithm to cluster large spatial databases, and two sampling based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN, and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large scale spatial databases. 关 键 词：SPATIAL DATABASES data MINING CLUSTERING sampling DBSCAN algorithm