Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has been widely applied in many areas of science due to its simplicity, robustness against noise (outlier) and ability to discover clusters of arbitrary shapes. However, DBSCAN algorithm requires two initial input parameters, namely Eps (the radius of the cluster) and MinPts (the minimum data objec...

The DBSCAN [1] algorithm is a popular algorithm in Data Mining field as it has the ability to mine the noiseless arbitrary shape Clusters in an elegant way. As the original DBSCAN algorithm uses the distance measures to compute the distance between objects, it consumes so much processing time and its computation complexity comes as O (N). In this paper we have proposed a new algorithm to improv...

DBSCAN is a typically used clustering algorithm due to its clustering ability for arbitrarily-shaped clusters and its robustness to outliers. Generally, the complexity of DBSCAN is O(n) in the worst case, and it practically becomes more severe in higher dimension. Grid-based DBSCAN is one of the recent improved algorithms aiming at facilitating efficiency. However, the performance of grid-based...

Software reuse is the process of implementing or updating software systems using existing software assets. Anything that is produced from a software development effort can potentially be reused. In this study, the performance of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is evaluated for Reusability Prediction of Function based Software systems. Here, the metric ba...

We present a new algorithm for the widely used density-based clustering method dbscan. Our algorithm computes the dbscan-clustering in O(n log n) time in R, irrespective of the scale parameter ε (and assuming the second parameter MinPts is set to a fixed constant, as is the case in practice). Experiments show that the new algorithm is not only fast in theory, but that a slightly simplified vers...

This paper describes the incremental behaviours of Density based clustering. It specially focuses on the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and its incremental approach.DBSCAN relies on a density based notion of clusters.It discovers clusters of arbitrary shapes in spatial databases with noise.In incremental approach, the DBSCAN algorithm is applied t...

We present PS-DBSCAN, a communication ecient parallel DBSCAN algorithm that combines the disjoint-set data structure and Parameter Server framework in Platform of AI (PAI). Since data points within the same cluster may be distributed over di‚erent workers which result in several disjoint-sets, merging them incurs large communication costs. In our algorithm, we employ a fast global union approa...

In this work we propose an extension of the DBSCAN algorithm to generate clusters with fuzzy density characteristics. The original version of DBSCAN requires two parameters (minPts and ) to determine if a point lies in a dense area or not. Merging different dense areas results into clusters that fit the underlined dataset densities. In this approach, a single density threshold is employed for a...