Density based clustering algorithm is one of the primary methods for clustering in data mining. The clusters which are formed based on the density are easy to understand and it does not limit itself to the shapes of clusters. This paper gives a detailed survey of the existing density based algorithms namely DBSCAN, VDBSCAN, DVBSCAN, ST-DBSCAN and DBCLASD based on the essential parameters needed...

The density peaks clustering (DPC) algorithm is a novel density-based clustering approach. Outliers can be spotted and excluded automatically, and clusters can be found regardless of the shape and of dimensionality of the space in which they are embedded. However, it still has problems when processing a complex data set with irregular shapes and varying densities to get a good clustering result...

Finding clusters of different densities is a challenging task. DBSCAN “Density-Based Spatial Clustering Applications with Noise” method has trouble discovering various since it uses fixed radius. This article proposes an extended for finding densities. The proposed dynamic radius and assigns regional density value each object, then counts the objects similar within If neighborhood size ≥ MinPts...

However, in general, the parameters of density-based clustering algorithms are usually difficult to select. So, in order to make the density-based clustering algorithms more robust, the extension with fuzzy set theory has attracted a lot of attentions recently. The fuzzy neighborhood DBSCAN (FNDBSCAN) is a typical one with this idea. But FN-DBSCAN usually requires a time complexity of O(n2) whe...

Density-based clustering algorithms are a widely-used class of data mining techniques that can find irregularly shaped clusters and cluster data without prior knowledge of the number of clusters the data contains. DBSCAN is the most well-known density-based clustering algorithm. We introduce our extension of DBSCAN, called Mr. Scan, which uses a hybrid/hybrid parallel implementation that combin...

We propose a simple and efficient modification of the popular DBSCAN clustering algorithm. This modification is able to detect the most interesting vertical threshold level in an automated, data-driven way. We establish both consistency and optimal learning rates for this modification.

the traditional algorithms do not meet the latest multiple requirements simultaneously for objects. Density-based method is one of the methodologies, which can detect arbitrary shaped clusters where clusters are defined as dense regions separated by low density regions. In this paper, we present a new clustering algorithm to enhance the density-based algorithm DBSCAN. This enables an automatic ...