Diabetic Retinopathy (DR) is the third biggest cause of blindness in India. Hard exudates are the primary signs of DR. In this paper the authors propose a novel hybrid mechanism for the detection of Exudates based DBSCAN clustering algorithm. Unlike other clustering algorithms, DBSCAN clustering does not require the number of clusters to be specified. Classification of regions is being done usi...

Big data is often mined using clustering algorithms. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a popular spatial clustering algorithm. However, it is computationally expensive and thus for clustering big data, parallel processing is required. The two prevalent paradigms for parallel processing are High-Performance Computing (HPC) based on Message Passing Interface ...

DBSCAN is a very classic algorithm for data clustering, which is widely used in many fields. However, with the data scale growing much more bigger than before, the traditional serial algorithm can not meet the performance requirement. Recently, parallel computing based on CUDA has developed very fast and has great advantage on big data. This paper summarizes the algorithms proposed before and i...

Clustering in data mining is a discovery process that groups a set of data objects so that the inter-cluster similarity is minimized and intracluster similarity is maximized. In presence of noise and outlier in high dimensional data base it is a difficult task to find out the clusters of different shapes, sizes and differ in density. Density based clustering algorithms like DBSCAN finds the clu...

The clustering is the technique in which similar and dissimilar type of data is clustered in different clusters for batter analysis of the input data. The algorithm of DBSCAN is applied in which EPS is calculated which will be the central point and from the central point Euclidean distance is calculated to define similarity and dissimilarity of the input data. In the existing algorithm EPS is c...

This article presents the modeling, development and theoretical grounding for the development of an application based on the clustering algorithm DBSCAN, aiming to reduce the daily waste of time on the locomotion of a huge number of people to a common place. The clusters are created based on attributes, like the departure time of each person from its residence, the final destine and its both ge...

Finding clusters with widely differing sizes, shapes and densities in presence of noise and outliers is a challenging job. The DBSCAN is a versatile clustering algorithm that can find clusters with differing sizes and shapes in databases containing noise and outliers. But it cannot find clusters based on difference in densities. We extend the DBSCAN algorithm so that it can also detect clusters...

Traditionally it had been a problem that researchers did not have access to enough spatial data to answer pressing research questions or build compelling visualizations. Today, however, the problem is often that we have too much data. Spatially redundant or approximately redundant points may refer to a single feature (plus noise) rather than many distinct spatial features. We can use density-ba...

This paper proposes a novel clustering methodology which undeniably manages to offer results with a higher inter-cluster inertia for a better clustering. The advantage obtained with this methodology is due to an algorithm that showed beforehand its efficiency in clustering exercises, MCDBSCAN, which is associated to an iterative process with a potential of auto-adjustment of the weights of the ...

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996), and has the following advantages: first, Greedy algorithm substitutes for R(*)-tree (Bechmann et al., 1990) in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory ...