In professional soccer, increasing amounts of data are collected that harness great potential
when it comes to analysing tactical behaviour. Unlocking this potential is difficult as big data …

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 …

Subgroup discovery is a broadly applicable descriptive data mining technique for identifying
interesting subgroups according to some property of interest. This article summarizes …

Finding subsets of a dataset that somehow deviate from the norm, ie where something
interesting is going on, is a classical Data Mining task. In traditional local pattern mining …

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 …

Identifying the factors that influence student academic performance is essential to provide
timely and effective support interventions. The data collected during enrolment and after …

Subgroup discovery is a key data mining method that aims at identifying descriptions of
subsets of the data that show an interesting distribution with respect to a pre-defined target …

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

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 …

Subgroup discovery (SD) is an exploratory pattern mining paradigm that comes into its own
when dealing with large real-world data, which typically involves many attributes, of a …