We show that DBSCAN can estimate the connected components of the λ-density level set {x : f(x) ≥ λ} given n i.i.d. samples from an unknown density f . We characterize the regularity of the level set boundaries using parameter β > 0 and analyze the estimation error under the Hausdorff metric. When the data lies in R we obtain a rate of Õ(n−1/(2β+D)), which matches known lower bounds up to logari...

In this contribution we provide an application of the technique of F–transform. We demonstrate that by using a simple density-based preprocessing, the applicability of F–transform in data analysis can be significantly improved. Despite of the fact that our procedure is demonstrated by a well-known DBSCAN algorithm and the technique of F–transform, the ideas are general enough to be applied for ...

This paper presents the review of various techniques which are used for clustering in data mining.Clustering is the process of dividing data into group of similar objects.Clustering involves various techniques so that the data can be partitioned into groups of same data like k-means algorithm, k-medoid, BIRCH algorithm, chameleon, CLIQUE algorithm, DBSCAN algorithm. In this paper, the overview ...

At present, the core of lidar data registration algorithms depends on search correspondence, which has become factor limiting performance this kind algorithm. For point-based algorithms, coincidence rate is too low, and for line-based method searching correspondence complex unstable. In paper, a laser radar algorithm based DBSCAN (Density-Based Spatial Clustering Applications with Noise) cluste...