Semantic subgroup explanations

作者: Anže Vavpetič , Vid Podpečan , Nada Lavrač

DOI: 10.1007/S10844-013-0292-1

关键词: Class (computer programming)Semantic data miningKEGGData miningGene expression profilingGene ontologyNatural language processingArtificial intelligenceComputer scienceDomain (software engineering)Ontology (information science)

摘要: Subgroup discovery (SD) methods can be used to find interesting subsets of objects a given class. While subgroup describing rules are themselves good explanations the subgroups, domain ontologies provide additional descriptions data and alternative constructed rules. Such in terms higher level ontology concepts have potential providing new insights into investigation. We show that this explanatory power ensured by using recently developed semantic SD methods. present approach explaining subgroups through demonstrate its utility on motivational use case gene expression profiling where groups patients, identified expression, further explained from Gene Ontology KEGG orthology. qualitatively compare methodology with supporting factors technique for characterizing subgroups. The tools implemented within browser-based mining platform ClowdFlows.

参考文章(39)
Nada Lavrač, Anže Vavpetič, Larisa Soldatova, Igor Trajkovski, Petra Kralj Novak, Using ontologies in semantic data mining with SEGS and g-SEGS discovery science. pp. 165- 178 ,(2011) , 10.1007/978-3-642-24477-3_15
Einoshin Suzuki, Autonomous discovery of reliable exception rules knowledge discovery and data mining. pp. 259- 262 ,(1997)
Agnieszka Ławrynowicz, Jedrzej Potoniec, Fr-ONT: an algorithm for frequent concept mining with formal ontologies international syposium on methodologies for intelligent systems. pp. 428- 437 ,(2011) , 10.1007/978-3-642-21916-0_46
Melanie Hilario, Phong Nguyen, Huyen Do, Adam Woznica, Alexandros Kalousis, Ontology-Based Meta-Mining of Knowledge Discovery Workflows Meta-Learning in Computational Intelligence. pp. 273- 315 ,(2011) , 10.1007/978-3-642-20980-2_9
Stefan Wrobel, An Algorithm for Multi-relational Discovery of Subgroups european conference on principles of data mining and knowledge discovery. pp. 78- 87 ,(1997) , 10.1007/3-540-63223-9_108
Janez Kranjc, Vid Podpečan, Nada Lavrač, ClowdFlows: A Cloud Based Scientific Workflow Platform Machine Learning and Knowledge Discovery in Databases. pp. 816- 819 ,(2012) , 10.1007/978-3-642-33486-3_54
Marko Robnik-Šikonja, Igor Kononenko, Theoretical and Empirical Analysis of ReliefF and RReliefF Machine Learning. ,vol. 53, pp. 23- 69 ,(2003) , 10.1023/A:1025667309714
Branko Kavšek, Nada Lavrač, Viktor Jovanoski, APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery Advances in Intelligent Data Analysis V. ,vol. 20, pp. 230- 241 ,(2003) , 10.1007/978-3-540-45231-7_22
Stephen D. Bay, Michael J. Pazzani, Detecting Group Differences: Mining Contrast Sets Data Mining and Knowledge Discovery. ,vol. 5, pp. 213- 246 ,(2001) , 10.1023/A:1011429418057
Martin Atzmueller, Frank Puppe, SD-Map – A Fast Algorithm for Exhaustive Subgroup Discovery Lecture Notes in Computer Science. pp. 6- 17 ,(2006) , 10.1007/11871637_6