Since the operative definition given by CWJ Granger of an idea expressed by N. Wiener, the
Wiener-Granger causality (WGC) has been one of the most relevant concepts exploited by …

Despite the development of large-scale data-acquisition techniques, experimental
observations of complex systems are often limited to a tiny fraction of the system under …

Clustering is a powerful technique in data-mining, which involves identifing homogeneous
groups of objects based on the values of attributes. Meta-heuristic algorithms such as …

As the amount of information recorded and stored electronically grows ever larger, it
becomes increasingly useful, if not essential, to develop better and more efficient ways to …

Given a set of entities, Cluster Analysis aims at finding subsets, called clusters, which are
homogeneous and/or well separated. As many types of clustering and criteria for …

In science, engineering and economics, decision problems are frequently modelled by
optimizing the value of a (primary) objective function under stated feasibility constraints. In …

This paper presents an ant colony optimization methodology for optimally clustering N
objects into K clusters. The algorithm employs distributed agents which mimic the way real …

To explain the prediction result of a Deep Neural Network (DNN) model based on a given
sample, LIME [1] and its derivatives have been proposed to approximate the local behavior …

Discovering a meaningful symbolic expression that explains experimental data is a
fundamental challenge in many scientific fields. We present a novel, open-source …

The clustering problem has been studied by many researchers using various approaches,
including tabu searching, genetic algorithms, simulated annealing, ant colonies, a …