For several years, parts of the content of this textbook have been used in undergraduate
courses in the Department of Mathematics and in the Faculty of Economics at the University …

In this paper, we propose a modification of the well-known DBSCAN algorithm, which
recognizes clusters with various data densities in a given set of data points A={a^ i ∈ R^ n …

A possibility of applying the density-based clustering algorithm Rough-DBSCAN for
earthquake zoning is considered in the paper. By using density-based clustering for …

One of the most common problems in recommender systems is a “cold-start” problem, which
is related to users who do not indicate any behavior on social media. This paper proposes a …

Given the large-scale exploitation and utilization of wind power, the problems caused by the
high stochastic and random characteristics of wind speed make researchers develop more …

Density-based spatial clustering of applications with noise (DBSCAN) algorithm can find
clusters of arbitrary shape, while the noise points can be removed. Membrane computing is …

Density-based spatial clustering of applications with noise (DBSCAN) is an unsupervised
classification algorithm which has been widely used in many areas with its simplicity and its …

For it can identify the clusters with any shape and tackle the boundary points effectively, the
typical density-based method of DBSCAN was widely applied to the clustering analysis. But …

DBSCAN algorithm is a density-based clustering algorithm, and it has been widely used in
data clustering. DBSCAN algorithm needs to calculate the distance between each object …

As a density clustering method, DBSCAN clustering algorithm can automatically determine
the number of clusters and effectively deal with the clusters of arbitrary shape, but the choice …