A Adaptive DBSCAN Algorithm Based on Gauss Distribution

Traditional DBSCAN algorithm usually set the threshold(minPts,Eps) depending on empirical value,and merely fit for single-density data sets,in allusion to the shortage,the article puts forward a new adaptive DBSCAN algorithm which based on Gaussian distribution and can be used to multi-density data sets.The new algorithm can generate proper threshold according to the data set characteristics.Firstly,to get the minPts accornding to the max CEI value.Then,to confirm the number of Eps according to the curve density level,so that to get Eps data by the Gaussian distribution law.At last,to make clustering for the data set with the minPts and Eps value.Also,to apply the new algorithm and the traditional DBSCAN algorithm to single-density and multi-density data sets,the results show that the new algorithm is more efficient.