Inducing decision trees via concept latticesFootnote1

^1. The paper is an extended version of a conference paper presented at CLA 2007, Montpellier, France, 24–26 October 2007.

References

• Carpineto , C. and Romano , G. 1996 . A lattice conceptual clustering system and its application to browsing retrieval . Machine learning , 24 : 95 – 122 .
   Web of Science ®Google Scholar
• Carpineto , C. and Romano , G. 2004 . Concept data analysis. Theory and applications , New York : Wiley .
   Google Scholar
• Dunham , M.H. 2003 . Data mining. Introductory and advanced topics , Upper Saddle River, NJ : Prentice Hall .
   Google Scholar
• Fayyad , U.M. , Piatetsky-Shapiro , G. and Smyth , P. 1996 . “ From Data mining to knowledge discovery: an overview ” . In Advances in knowledge discovery and data mining , Edited by: Fayyad , U.M. , Piatetsky-Shapiro , G. , Smyth , P. and Uthurusamy , R. 3 – 33 . Melno Park, CA : AAAI Press .
   Google Scholar
• Fu , H. 2004 . “ A comparative study of FCA-based supervised classification algorithms ” . In ICFCA 2004 , Edited by: Eklund , P. 313 – 320 . Berlin/Heidelberg : Springer-Verlag . Lecture notes in artificial intelligence 2961
   Google Scholar
• Ganter , B. and Wille , R. 1999 . Formal concept analysis. Mathematical foundations , Berlin : Springer .
   Google Scholar
• Kuznetsov , S.O. 2004 . “ Machine learning and formal concept analysis ” . In ICFCA 2004 , Edited by: Eklund , P. 287 – 312 . Berlin/Heidelberg : Springer-Verlag . Lecture notes in artificial intelligence 2961
   Google Scholar
• Lindig , C. 2000 . “ Fast concept analysis ” . In Working with conceptual structures –contributions to ICCS 2000 , Edited by: Stumme , G. 152 – 161 . Aachen : Shaker Verlag .
   Google Scholar
• Mephu Nguifo , E. and Njiwoua , P. 2001 . IGLUE: a lattice-based constructive induction system . Intelligent data analysis , 5 ( 1 ) : 73 – 91 .
   Google Scholar
• Mitchell , T.M. 1997 . Machine learning 1997 McGraw-Hill, New York
   Google Scholar
• Murthy , S.K. 1998 . Automatic construction of decision trees from data . Data mining and knowledge discovery , 2 : 345 – 389 .
   Web of Science ®Google Scholar
• Newman , D.J. 1998 . UCI repository of machine learning databases , Irvine, CA : University of California, Department of Information and Computer Science . Available from: http://www.ics.uci.edu/∼mlearn/MLRepository.html
   Google Scholar
• Pasquier , N. 1999 . Efficient mining of association rules using closed itemset lattices . Information systems , 24 ( 1 ) : 25 – 46 .
   Web of Science ®Google Scholar
• Quinlan , J.R. 1993 . C4.5: programs for machine learning , San Francisco, CA : Morgan Kaufmann .
   Google Scholar
• Quinlan , J.R. 1996 . Learning decision tree classifiers . ACM computing surveys , 28 ( 1 ) : 71 – 72 .
   Web of Science ®Google Scholar
• Tan , P.N. , Steinbach , M. and Kumar , V. 2006 . Introduction to data mining , Boston, MA : Addison Wesley .
   Google Scholar
• Witten , I.H. and Frank , E. 2005 . Data mining: practical machine learning tools and techniques , 2nd ed. , San Francisco, CA : Morgan Kaufmann .
   Google Scholar