Design and Implementation of an Improved DBSCAN Algorithm
Peihong Lin, Hong Yuan Zheng, Wenbo Feng, Yihao Li, Lifa Wu · 2019
DBSCAN algorithm is a density-based clustering algorithm, and it has been widely used in data clustering. DBSCAN algorithm needs to calculate the distance between each object and all the other objects when searching for core objects. The process leads to high computational overhead. This paper proposes an improved algorithm which takes no account of distant neighbors of an object and computes only the distances between the object and its nearby neighbors. This method reduces the distance measurements when searching for core objects, and the computational cost is obviously reduced. Four standard data sets of UCI are selected to verify the clustering process and algorithm performance of the improved DBSCAN algorithm. Experimental results show that the improved algorithm reduces the computational cost tremendously, while maintaining high clustering accuracy.