A statistical information-based clustering approach in distance space

Clustering, as a powerful data mining technique for discovering interesting data distributions and patterns in the nderlying database, is used in many fields, such as statistical data analysis, pattern recognition, image processing, and other usiness applications. Density-based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996) is a good erformance clustering method for dealing with spatial data although it leaves many problems to be solved. For example, BSCAN requires a necessary user-specified threshold while its computation is extremely time-consuming by current method uch as OPTICS, etc. (Ankerst et al., 1999), and the performance of DBSCAN under different norms has yet to be examined. In his paper, we first developed a method based on statistical information of distance space in database to determine the necessary hreshold. Then our examination of the DBSCAN performance under different norms showed that there was determinable relation etween them. Finally, we used two artificial databases to verify the effectiveness and efficiency of the proposed methods. ey words: DBSCAN algorithm, Statistical information, Threshold