Nowadays, there is an incredible increase of data volumes around the world, with the
Internet as one of the main actors in this scenario and a growth rate above 30GB/s. The …

A new multi-objective evolutionary model for subgroup discovery with fuzzy rules is
presented in this paper. The method resolves subgroup discovery problems based on the …

In this paper a new evolutionary multi-objective algorithm (GAR-SD) for Subgroup Discovery
tasks is presented. This algorithm can work with both discrete and continuous attributes …

Subgroup discovery is a well-known technique for the extraction of patterns, with respect to a
variable of interest in the data. However, the explosion in data gathering has hampered the …

A non-dominated multiobjective evolutionary algorithm for extracting fuzzy rules in subgroup
discovery (NMEEF-SD) is described and analyzed in this paper. This algorithm, which is …

Subgroup discovery (SD) is a descriptive data mining technique using supervised learning.
In this article, we review the use of evolutionary algorithms (EAs) for SD. In particular, we will …

This paper presents a genetic fuzzy system for the data mining task of subgroup discovery,
the subgroup discovery iterative genetic algorithm (SDIGA), which obtains fuzzy rules for …

Subgroup discovery is a data mining technique that discovers interesting associations
among different variables with respect to a property of interest. Existing subgroup discovery …

Large data is challenging for most existing discovery algorithms, for several reasons. First of
all, such data leads to enormous hypothesis spaces, making exhaustive search infeasible …

This paper presents an evolutionary approach for mining diverse and more informative
subgroups focused on high dimensional data sets. Subgroup Discovery (SD) is an important …