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Density-based clustering has found numerous applications across variousdomains. The Density-Based Spatial Clustering of Applications with Noise(DBSCAN) algorithm is capable of finding clusters of varied shapes that are notlinearly separable, at the same time it is not sensitive to outliers in thedata. Combined with the fact that the number of clusters in the data are notrequired apriori makes DBSCAN really powerfully. Slower performance (O(n2))limits its applications. In this work, we present a new clustering algorithm,the HyperCube Accelerated DBSCAN(HCA-DBSCAN) which uses a combination ofdistance-based aggregation by overlaying the data with customized grids. We userepresentative points to reduce the number of comparisons that need to becomputed. Experimental results show that the proposed algorithm achieves asignificant run time speedup of up to 58.27% when compared to otherimprovements that try to reduce the time complexity of theDBSCAN algorithm

HCA-DBSCAN: HyperCube Accelerated Density Based Spatial Clustering for Applications with Noise