Frequent itemset mining (FIM) is an essential task within data analysis since it is responsible
for extracting frequently occurring events, patterns, or items in data. Insights from such …

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 …

Rules–the clearest, most explored and best understood form of knowledge representation–
are particularly important for data mining, as they offer the best tradeoff between human and …

This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and
subgroup discovery (SD) in a unifying framework named supervised descriptive rule …

This paper investigates how to adapt standard classification rule learning approaches to
subgroup discovery. The goal of subgroup discovery is to find rules describing subsets of the …

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

Many different metrics are used in machine learning and data mining to build and evaluate
models. However, there is no general theory of machine learning metrics, that could answer …

This paper provides an analysis of the behavior of separate-and-conquer or covering rule
learning algorithms by visualizing their evaluation metrics and their dynamics in coverage …

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

This paper presents a subgroup discovery algorithm APRIORI-SD, developed by adapting
association rule learning to subgroup discovery. The paper contributes to subgroup …