An Improved DBSCAN Algorithm
Yan Zhenxing · Journal of Sichuan Normal University · 2013
Clustering is an important technique in data mining,which can classify data according to the characteristic of data.DBSCAN is a classical density-based clustering algorithm,which can automatically determine the number of clusters and deal with clusters of arbitrary shapes,however it needs to specify two parameters of Eps and MinPts before clustering and the clustering results are very sensitive to the two parameters.In this paper an improved DBSCAN algorithm is proposed,which can specify Eps adaptively to deal with data sets with different density clusters.Experimental results demonstrate effectiveness of the improved algorithm.