An Improved DBSCAN Algorithm Based on the Neighbor Similarity and Fast Nearest Neighbor Query

DBSCAN is the most famous density based clustering algorithm which is one of the main clustering paradigms. However, there are many redundant distance computations among the process of DBSCAN clustering, due to brute force Range-Query used to retrieve neighbors for each point in DBSCAN, which yields high complexity (O(n
²
)) and low efficiency. Thus, it is unsuitable and not applicable for large scale data. In this paper, an improved DBSCAN based on neighbor similarity is proposed, which utilizes and Cover Tree to retrieve neighbors for each point in parallel, and the triangle inequality to filter many unnecessary distance computations. From the experiments conducted on large scale data sets, it is demonstrated that the proposed algorithm greatly speedup the original DBSCAN, and outperform the main improvements of DBSCAN. Comparing with ρ-approximate DBSCAN, which is the current fastest but approximate version of DBSCAN, the proposed algorithm has two advantages: one is faster and the other is that the result is accurate.