Partition-based DBSCAN algorithm with different parameter

Clustering is one of the most important research fields in data mining.DBSCAN is a density based clustering algorithm.This algorithm is capable of clustering high density areas and finding arbitrary clusters in spatial database with noise.However,when DBSCAN is analyized,it is found that when data distribution is not even,clustering quality degrades for using the same global variable.In this paper,aimming at this weakness,a data partition based algorithm is proposed.For each local dataset,different variables are adopted,and clustering is done separately.At last local clustering results are merged.The experimental result demonstrates that the improved al-gorithm is effective and feasible.