A popular unsupervised learning method, known as clustering, is extensively used in data
mining, machine learning and pattern recognition. The procedure involves grouping of …

We analyze the drawbacks of DBSCAN and its variants, and find the grid technique, which is
used in Fast-DBSCAN and ρ-approximate DBSCAN, is almost useless in high dimensional …

A new density-based clustering algorithm, RNN-DBSCAN, is presented which uses reverse
nearest neighbor counts as an estimate of observation density. Clustering is performed …

Large-scale data clustering is an essential key for big data problem. However, no current
existing approach is “optimal” for big data due to high complexity, which remains it a great …

Data mining is used for finding meaningful information out of a vast expanse of data. With
the advent of Big Data concept, data mining has come to much more prominence …

DBSCAN is the most famous density based clustering algorithm which is one of the main
clustering paradigms. However, there are many redundant distance computations among …

Density-based clustering has several desirable properties, such as the abilities to handle
and identify noise samples, discover clusters of arbitrary shapes, and automatically discover …

Clustering analysis has been widely used in statistics, machine learning, pattern recognition,
image processing, and so on. It is a great challenge for most existing clustering algorithms to …

The DBSCAN method for spatial clustering has received significant attention due to its
applicability in a variety of data analysis tasks. There are fast sequential algorithms for …

Clustering is a typical data mining technique that partitions a dataset into multiple subsets of
similar objects according to similarity metrics. In particular, density-based algorithms can find …