Subgroup discovery is a data mining technique which extracts interesting rules with respect
to a target variable. An important characteristic of this task is the combination of predictive …

A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life
Problems Contrast Data Mining: Concepts, Algorithms, and Applications collects recent …

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 …

Discriminative patterns can provide valuable insights into data sets with class labels, that
may not be available from the individual features or the predictive models built using them …

Comparing groups in data is a common theme in corpus-level music analysis and in
exploratory data mining. Contrast patterns describe significant differences between groups …

Discriminative patterns are association patterns that occur with disproportionate frequency in
some classes versus others, and have been studied under names such as emerging …

In computational pattern discovery, pattern evaluation measures select or rank patterns
according to their potential interestingness in a given analysis task. Many measures have …

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 …

The current fraud detection system at Rabobank suffers from high false alarm rate.
Therefore, a Machine Learning (ML) model has been introduced to learn the behaviour of …

Data classification is a challenging task in era of big data due to high number of features.
Feature selection is a step in process of knowledge discovery in data that aims to reduce …