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Journal of Computer Science and Technology 2016, Vol. 31 Issue (3) :561-576    DOI: 10.1007/s11390-016-1647-1
Data Management and Data Mining Current Issue | Archive | Adv Search << Previous Articles | Next Articles >>
Subgroup Discovery Algorithms: A Survey and Empirical Evaluation
Sumyea Helal
School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA5001, Australia

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Abstract Subgroup discovery is a data mining technique that discovers interesting associations among different variables with respect to a property of interest. Existing subgroup discovery methods employ different strategies for searching, pruning and ranking subgroups. It is very crucial to learn which features of a subgroup discovery algorithm should be considered for generating quality subgroups. In this regard, a number of reviews have been conducted on subgroup discovery. Although they provide a broad overview on some popular subgroup discovery methods, they employ few datasets and measures for subgroup evaluation. In the light of the existing measures, the subgroups cannot be appraised from all perspectives. Our work performs an extensive analysis on some popular subgroup discovery methods by using a wide range of datasets and by defining new measures for subgroup evaluation. The analysis result will help with understanding the major subgroup discovery methods, uncovering the gaps for further improvement and selecting the suitable category of algorithms for specific application domains.
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Sumyea Helal
Keywordssubgroup discovery   searching   pruning   measure   evaluation     
Received 2015-02-12;
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Sumyea Helal.Subgroup Discovery Algorithms: A Survey and Empirical Evaluation[J]  Journal of Computer Science and Technology, 2016,V31(3): 561-576
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http://jcst.ict.ac.cn:8080/jcst/EN/10.1007/s11390-016-1647-1
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