In data mining, the classical association rule mining techniques deal with binary attributes;
however, real-world data have a variety of attributes (numerical, categorical, Boolean). To …

Numerical association rule mining is a widely used variant of the association rule mining
technique, and it has been extensively used in discovering patterns and relationships in …

Large amounts of annual costs are made for safety and compensations of accidents in urban
intersections, even those with traffic lights. The main reason for accidents seems to be the …

Deep learning model design, development, and debugging is a process driven by best
practices, guidelines, trial-and-error, and the personal experiences of model developers. At …

In association rule mining, both the classical algorithms and today's available tools either
use binary data items or discretized data. However, in real-world scenarios, data are …

Statistical reasoning was one of the earliest methods to draw insights from data. However,
over the last three decades, association rule mining and online analytical processing have …

This article proposes a new algorithm for a newly emerging domain wisdom mining that
claims to extract wisdom from data. Association rule mining is one of the dominant data …

Many real-world data sets contain a mix of various types of data, ie, binary, numerical, and
categorical; however, many data mining and machine learning (ML) algorithms work merely …

Among the inherently interpretable learning algorithms are associative classifiers, which are
induced in steps. Regarding the ranking step, it is carried out using objective measures in …

A crucial characteristic of machine learning models in various domains (such as medical
diagnosis, financial analysis, or real-time process monitoring) is the interpretability. The …