A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method. (October 2016)

• Record Type: Journal Article
• Authors: Mahesh Kumar, K.; Rama Mohan Reddy, A.
• Is Part Of: Pattern recognition. Volume 58(2016:Oct.)
• Page Start: 39
• Page End: 48
• Publication Date: 2016-10
• Subjects: Unsupervised learning -- Density based clustering -- DBSCAN -- Neighborhood graph; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.03.008
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: Density based clustering methods are proposed for clustering spatial databases with noise. Density Based Spatial Clustering of Applications with Noise (DBSCAN) can discover clusters of arbitrary shape and also handles outliers effectively. DBSCAN obtains clusters by finding the number of points within the specified distance from a given point. It involves computing distances from given point to all other points in the dataset. The conventional index based methods construct a hierarchical structure over the dataset to speed-up the neighbor search operations. The hierarchical index-structures fail to scale for datasets of dimensionality above 20. In this paper, we propose a novel graph-based index structure method Groups that accelerates the neighbor search operations and also scalable for high dimensional datasets. Experimental results show that the proposed method improves the speed of DBSCAN by a factor of about 1.5–2.2 on benchmark datasets. The performance of DBSCAN degrades considerably with noise due to unnecessary distance computations introduced by noise points while the proposed method is robust to noise by pruning out noise points early and eliminating the unnecessary distance computations. The cluster results produced by our method are exactly similar to that of DBSCAN but executed at a much faster pace. Highlights: A graph-based index structure is built for speeding up neighbor search operations. No additional inputs are required to build the index structure. Proposed method is scalable for high-dimensional datasets. Handles noise effectively to improve the performance of DBSCAN.