Explaining subgroups through ontologies

Authors:
Anže Vavpetič;Vid Podpečan;Stijn Meganck;Nada Lavrač
Affiliations:
Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia;Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia;Computational Modeling Lab, Vrije Universiteit Brussel, Brussel, Belgium;Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia, University of Nova Gorica, Nova Gorica, Slovenia
Venue:
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Year:
2012

Citing 15
Cited 0

Explora: a multipattern and multistrategy discovery assistant

Advances in knowledge discovery and data mining
Efficient mining of emerging patterns: discovering trends and differences

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting Group Differences: Mining Contrast Sets

Data Mining and Knowledge Discovery
An Algorithm for Multi-relational Discovery of Subgroups

PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Theoretical and Empirical Analysis of ReliefF and RReliefF

Machine Learning
On detecting differences between groups

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Subgroup Discovery with CN2-SD

The Journal of Machine Learning Research
SEGS: Search for enriched gene sets in microarray data

Journal of Biomedical Informatics
Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining

The Journal of Machine Learning Research
Expert-guided subgroup discovery: methodology and application

Journal of Artificial Intelligence Research
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter
Using ontologies in semantic data mining with SEGS and g-SEGS

DS'11 Proceedings of the 14th international conference on Discovery science
SD-map: a fast algorithm for exhaustive subgroup discovery

PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
inSilicoDb

Bioinformatics
Orange4WS Environment for Service-Oriented Data Mining

The Computer Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Subgroup discovery (SD) methods can be used to find interesting subsets of objects of a given class. Subgroup descriptions (rules) are themselves good explanations of the subgroups. Domain ontologies provide additional descriptions to data and can provide alternative explanations of discovered rules; such explanations in terms of higher level ontology concepts have the potential of providing new insights into the domain of investigation. We show that this additional explanatory power can be ensured by using recently developed semantic SD methods. We present the new approach to explaining subgroups through ontologies and demonstrate its utility on a gene expression profiling use case where groups of patients, identified through SD in terms of gene expression, are further explained through concepts from the Gene Ontology and KEGG orthology.