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 …

This paper provides a short overview of space–time series clustering, which can be
generally grouped into three main categories such as: hierarchical, partitioning-based, and …

Existing edge detection methods are based on fixed logics, which are not intelligent enough
to distinguish useful edges and useless/noise edges. Recent ellipse detection methods …

The use of different seismicity indicators as input for systems to predict earthquakes is
becoming increasingly popular. Nevertheless, the values of these indicators have not been …

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 …

The prediction of earthquakes is a task of utmost difficulty that has been widely addressed by
using many different strategies, with no particular good results thus far. Seismic time series …

The occurrences of earthquakes have no any explicit warning, and earthquake magnitude
prediction is still extremely challenging now. Therefore, this study proposes a seismic …

In this paper, the multiple ellipses detection problem on the basis of a data points set coming
from a number of ellipses in the plane not known in advance is considered. An ellipse is …

Seismical parameters of five seismogenic zonings for the Iberian Peninsula have been
determined in this work. For that purpose, this research has two key goals. The first is to …