Add to ORKGlocal exceptionality detection, interestingness measures, algorithms, exploratory data mining Subgroup discovery is a broadly applicable descriptive data mining technique for identifying interesting subgroups according to some property of interest. This article summarizes fundamentals of subgroup discovery, before it reviews algorithms and further advanced methodological issues. In addition, we briefly discuss tools and applications of subgroup discovery approaches. In that con-text, we also discuss experiences and lessons learned and outline future direc-tions in order to show the advantages and benefits of subgroup discovery
This paper presents an approach to expert-guided subgroup discovery. The main step of the subgroup d...
Although subgroup discovery aims to be a practical tool for exploratory data mining, its wider adopt...
This dissertation investigates how to adapt standard classification rule learning approaches to su...
Large volumes of data are collected today in many domains. Often, there is so much data available, t...
Abstract Subgroup discovery is a data mining technique which extracts interesting rules with respect...
Subgroup discovery is a data mining technique which extracts interesting rules with respect to a ta...
Large volumes of data are collected today in many domains. Often, there is so much data available, t...
Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-pop...
Subgroup discovery is the task of identifying the top k patterns in a database with most significant...
The discovery of (interesting) subgroups has a high practical relevance in all domains of science or...
Abstract. We propose an approach to subgroup discovery using distri-bution rules (a kind of associat...
Large data is challenging for most existing discovery algorithms, for several reasons. First of all,...
Deriving insights from high-dimensional data is one of the core problems in data mining. The difficu...
I Subgroup Discovery (SD) algorithms aim to find subgroups of data (represented by rules) that are s...
Subgroup discovery consists in finding subsets of individuals from a given population which have dis...
This paper presents an approach to expert-guided subgroup discovery. The main step of the subgroup d...
Although subgroup discovery aims to be a practical tool for exploratory data mining, its wider adopt...
This dissertation investigates how to adapt standard classification rule learning approaches to su...
Large volumes of data are collected today in many domains. Often, there is so much data available, t...
Abstract Subgroup discovery is a data mining technique which extracts interesting rules with respect...
Subgroup discovery is a data mining technique which extracts interesting rules with respect to a ta...
Large volumes of data are collected today in many domains. Often, there is so much data available, t...
Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-pop...
Subgroup discovery is the task of identifying the top k patterns in a database with most significant...
The discovery of (interesting) subgroups has a high practical relevance in all domains of science or...
Abstract. We propose an approach to subgroup discovery using distri-bution rules (a kind of associat...
Large data is challenging for most existing discovery algorithms, for several reasons. First of all,...
Deriving insights from high-dimensional data is one of the core problems in data mining. The difficu...
I Subgroup Discovery (SD) algorithms aim to find subgroups of data (represented by rules) that are s...
Subgroup discovery consists in finding subsets of individuals from a given population which have dis...
This paper presents an approach to expert-guided subgroup discovery. The main step of the subgroup d...
Although subgroup discovery aims to be a practical tool for exploratory data mining, its wider adopt...
This dissertation investigates how to adapt standard classification rule learning approaches to su...