Analysing massive amounts of data and extracting value from it has become key across different disciplines. As the amounts of data grow rapidly, current approaches for data analysis are no longer efficient. This is particularly true for clustering algorithms where distance calculations between pairs of points dominate overall time: the more data points are in the dataset, the bigger the share o...

Finding clustering patterns in data is challenging when clusters can be of arbitrary shapes and the data contains high percentage (e.g., 80%) of noise. This paper presents a novel technique named density-based multiscale analysis for clustering (DBMAC) that can conduct noise-robust clustering without any strict assumption on the shapes of clusters. Firstly, DBMAC calculates the r-neighborhood s...

In order to identify maize purity rapidly and efficiently, the image processing technology and clustering algorithm were studied and explored in depth focused on the maize seed and characteristics of the seed images. An improved DBSCAN on the basis of farthest first traversal algorithm (FFT) adapting to maize seeds purity identification was proposed in the paper. The color features parameters o...

Wide variety of methods had been designed under the cluster analysis; an unsupervised learning, like partitioning based, hierarchical, density based, model based, etc. DBSCAN, one of the most widely applied density based clustering algorithm outperforms partitioning based clustering algorithms such as k-means, CLARA, CLARANS and hierarchical algorithms, as it does not require a prior knowledge ...

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...