Editing for the k-nearest neighbors rule by a genetic algorithm
Pattern Recognition Letters - Special issue on genetic algorithms
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
On Issues of Instance Selection
Data Mining and Knowledge Discovery
Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
Data Mining and Knowledge Discovery
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Descriptive Induction through Subgroup Discovery: A Case Study in a Medical Domain
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Generating Actionable Knowledge by Expert-Guided Subgroup Discovery
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Adapting classification rule induction to subgroup discovery
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Stratification for scaling up evolutionary prototype selection
Pattern Recognition Letters
Propositionalization-based relational subgroup discovery with RSD
Machine Learning
Computers and Operations Research
A novel feature selection algorithm for text categorization
Expert Systems with Applications: An International Journal
An integrated approach for operational knowledge acquisition of refuse incinerators
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Expert-guided subgroup discovery: methodology and application
Journal of Artificial Intelligence Research
An efficient data mining approach for discovering interesting knowledge from customer transactions
Expert Systems with Applications: An International Journal
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
Subgroup discovery techniques and applications
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Multiobjective evolutionary induction of subgroup discovery fuzzy rules: a case study in marketing
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Searching for rules to detect defective modules: A subgroup discovery approach
Information Sciences: an International Journal
Hi-index | 12.05 |
The subgroup discovery, domain of application of CN2-SD, is defined as: ''given a population of individuals and a property of those individuals, we are interested in finding a population of subgroups as large as possible and have the most unusual statistical characteristic with respect to the property of interest''. The subgroup discovery algorithm CN2-SD, based on a separate and conquer strategy, has to face the scaling problem which appears in the evaluation of large size data sets. To avoid this problem, in this paper we propose the use of instance selection algorithms for scaling down the data sets before the subgroup discovery task. The results show that CN2-SD can be executed on large data set sizes pre-processed, maintaining and improving the quality of the subgroups discovered.