The increasing availability of vehicular trajectory data is at the core of smart mobility
solutions. Such data offer us unprecedented information for the development of trajectory …

Our ability to collect “big data” has greatly surpassed our capability to analyze it,
underscoring the emergence of the fourth paradigm of science, which is datadriven …

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 clustering is one of the most studied data mining tasks. It aims, through various
methods, to discover previously unknown groups within the data sets. In the past years …

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 trajectory data is an important way to mine hidden information behind moving
object sampling data, such as understanding trends in movement patterns, gaining high …

This paper presents new parallel algorithms for generating Euclidean minimum spanning
trees and spatial clustering hierarchies (known as HDBSCAN*). Our approach is based on …

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 …

Datasets collecting the ever-changing position of moving individuals are usually big and
possess high spatial and temporal resolution to reveal activity patterns of individuals in …

OPTICS is a density-based clustering method that can address point sets with different
densities; however, the outlier detection capability of OPTICS is limited by several factors …