Cluster analysis constitutes a pivotal component of database mining, with DBSCAN being
one of the most extensively employed algorithms in this domain. Nevertheless, DBSCAN is …

The density-based clustering method is considered a robust approach in unsupervised
clustering technique due to its ability to identify outliers, form clusters of irregular shapes and …

DBSCAN is a typically used clustering algorithm due to its clustering ability for arbitrarily-
shaped clusters and its robustness to outliers. Generally, the complexity of DBSCAN is O (n …

Cluster detection is important and widely used in a variety of applications, including public
health, public safety, transportation, and so on. Given a collection of data points, we aim to …

DBSCAN is a density-based clustering technique, well appropriate to discover clusters of
arbitrary shape, and to handle noise. The number of clusters does not have to be known in …

The density-based clustering algorithm DBSCAN is a state-of-the-art data clustering
technique with numerous applications in many fields. However, its O (n2) time complexity …

Cluster analysis in data mining and knowledge discovery is an essential business
application. This investigation describes a new clustering approach named EIDBSCAN that …

The density-based clustering is utilized for various applications such as hot spot detection or
segmentation. To serve those applications in real time, it is desired to update clusters …

The density-based clustering algorithm DBSCAN is a state-of-the-art data clustering
technique with numerous applications in many fields. However, DBSCAN requires …

DBSCAN is a fundamental density-based clustering algorithm with extensive applications.
However, a bottleneck of DBSCAN is its O (n2) worst-case time complexity. In this paper, we …