Despite the wide application of evolutionary computation (EC) techniques to rule discovery
in stock algorithmic trading (AT), a comprehensive literature review on this topic is …

Statistically sound pattern discovery harnesses the rigour of statistical hypothesis testing to
overcome many of the issues that have hampered standard data mining approaches to …

In Data Mining, the usefulness of association rules is strongly limited by the huge amount of
delivered rules. To overcome this drawback, several methods were proposed in the …

Relaxed functional dependencies (rfd s) are properties expressing important relationships
among data. Thanks to the introduction of approximations in data comparison and/or validity …

A number of studies, theoretical, empirical, or both, have been conducted to provide insight
into the properties and behavior of interestingness measures for association rule mining …

Temporal periodicity of patterns can be regarded as an important criterion for measuring the
interestingness of frequent patterns in several applications. A frequent pattern can be said …

With the advent of big data and data lakes, data are often integrated from multiple sources.
Such integrated data are often of poor quality, due to inconsistencies, errors, and so forth …

The progressive application of Information and Communication Technologies to industrial
processes has increased the amount of data gathered by manufacturing companies during …

Discovering causal relationships is the ultimate goal of many scientific explorations. Causal
relationships can be identified with controlled experiments, but such experiments are often …

Randomised controlled trials (RCTs) are the most effective approach to causal discovery,
but in many circumstances it is impossible to conduct RCTs. Therefore, observational …