This paper is the second part of a two-part paper, which is a survey of multiobjective
evolutionary algorithms for data mining problems. In Part I, multiobjective evolutionary …

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 …

Association rule discovery is an ever increasing area of interest in data mining. Finding rules
for attributes with numerical values is still a challenging point in the process of association …

The discovery of patterns that accurately discriminate one class label from another remains
a challenging data mining task. Subgroup discovery (SD) is one of the frameworks that …

This paper presents an evolutionary algorithm for Discriminative Pattern (DP) mining that
focuses on high dimensional data sets. DPs aims to identify the sets of characteristics that …

We address the problem of pattern discovery in vertex-attributed graphs. This kind of
structure consists of a graph augmented with attributes associated to vertices. Vertex …

It is a great challenge to companies, governments and researchers to extract knowledge in
high dimensional databases. Discriminative Patterns (DPs) is an area of data mining that …

Subgroup discovery is the most well-known task within the supervised descriptive pattern
mining field. It aims at discovering patterns in the form of rules induced from labeled data …

The main object of study in this thesis is subgroup discovery, a theoretical framework for
finding subgroups in data—ie, named sub-populations—whose behaviour with respect to a …

This chapter presents an overview of various nature-based algorithms to solve multi-
objective problems with the particular emphasis on Multi-Objective Evolutionary Algorithms …