selected publications
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article
- A new algorithm for computing reducts based on the binary discernibility matrix. Intelligent Data Analysis. 20:317-337. 2016-01-01
- On the relation between rough set reducts and typical testors. Information Sciences. 294:152-163. 2015-01-01
- Decision tree based classifiers for large datasets. Computacion y Sistemas. 17:95-102. 2013-01-01
- Building fast decision trees from large training sets. Intelligent Data Analysis. 16:649-664. 2012-01-01
- Decision tree induction using a fast splitting attribute selection for large datasets. Expert Systems with Applications. 38:14290-14300. 2011-01-01
- Determination of similarity threshold in clustering problems for large data sets. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2905:611-618. 2003-01-01
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conference paper
- Computing constructs by using typical testor algorithms. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 44-53. 2015-01-01
- Are reducts and typical testors the same?. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 294-301. 2014-01-01
- Easy categorization of attributes in decision tables based on basic binary discernibility matrix. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 302-310. 2013-01-01
- Multivariate decision trees using different splitting attribute subsets for large datasets. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 370-373. 2010-01-01
- A new incremental algorithm for induction of multivariate decision trees for large datasets. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 282-289. 2008-01-01
- LC: A conceptual clustering algorithm. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 117-127. 2001-01-01