Research on Improve DBSCAN Algorithm Based On Ant Clustering

Abstract

DBSCAN algorithm is sensitive to the input parameter of Eps, especially when the data density is non-uniform. It gets poor result in clustering using the same global Eps. In addition, the algorithm has difficulty with high-dimension of data. In this paper, an improved DBSCAN algorithm LF-DBSCAN is proposed, which uses ant clustering algorithm in data preprocessing phase to classify the datasets and to get several values of parameter Eps, then call DBSCAN algorithm with different values of Eps to cluster the non-uniform datasets. Experimental results demonstrate the effectiveness of the improved algorithm.