Subgroup discovery with CN2-SD

Journal of Machine Learning Research

Volumn 5, Issue , 2004, Pages 153-188

  Lavrač, Nada ^a,b   Kavšek, Branko ^a   Flach, Peter ^a   Todorovski, Ljupčo ^c  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b UNIVERSITY OF NOVA GORICA   (Slovenia)

^c UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

Rule learning; Subgroup discovery; Traffic accident data analysis; UCI data sets

Indexed keywords

HEURISTIC ALGORITHMS;

AREA UNDER ROC CURVE (AUC); CLASSIFICATION RULES; EXPERIMENTAL EVALUATION; PROBABILISTIC CLASSIFICATION; RULE LEARNING; SUBGROUP DISCOVERY; SUBSTANTIAL REDUCTION; UCI DATA SETS;

ACCIDENTS;

EID: 84925740795     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (44)

1. Rakesh Agrawal, Heikki Mannila, Ramakrishnan Srikant, Hannu Toivonen, and A. Inkeri Verkamo. Fast discovery of association rules. Advances in Knowledge Discovery and Data Mining, AAAI Press: 307-328, 1996.
2. Bojan Cestnik. Estimating probabilities: A crucial task in machine learning. In Proceedings of the Ninth European Conference on Artificial Intelligence, pages 147-149, Pitman, 1990.
3. Peter Clark and Robin Boswell. Rule induction with cn2: Some recent improvements. In Proceedings of the Fifth European Working Session on Learning, pages 151-163, Springer, 1991.
4. Peter Clark and Tim Niblett. The cn2 induction algorithm. Machine Learning, 3(4):261-283, 1989.
5. William W. Cohen. Fast effective rule induction. In Proceedings of the Twelfth International Conference on Machine Learning, pages 115-123, Morgan Kaufmann, 1995.
6. William W. Cohen and Yoram Singer. A simple, fast, and effective rule learner. In Proceedings of AAAI/IAAI, pages 335-342, AAAI Press, 1999.
7. Sašo Džeroski, Bojan Cestnik, and Igor Petrovski. Using the m-estimate in rule induction. Journal of Computing and Information Technology, 1(1):37-46, 1993.
8. Cesar Ferri-Ramírez, Peter A. Flach, and Jose Hernandez-Orallo. Learning decision trees using the area under the roc curve. In Proceedings of the Nineteenth International Conference on Machine Learning, pages 139-146, Morgan Kaufmann, 2002.
9. Peter A. Flach. The geometry of roc space: Understanding machine learning metrics through roc isometrics. In Proceedings of the Twentieth International Conference on Machine Learning, pages 194-201, AAAI Press, 2003.
10. Peter A. Flach and Iztok Savnik. Database dependency discovery: A machine learning approach. AI Communications, 12(3):139-160, 1999.
11. Yoav Freund and Robert E. Shapire. Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning, pages 148-156, Morgan Kaufmann, 1996.
12. Jerome H. Friedman and Nicholas I. Fisher. Bump hunting in high-dimensional data. Statistics and Computing, 9:123-143, 1999.
13. Johannes Fürnkranz and Peter A. Flach. An analysis of rule evaluation metrics. In Proceedings of the Twetieth International Conference on Machine Learning, pages 202-209, AAAI Press, 2003.
14. Dragan Gamberger and Nada Lavrač. Expert guided subgroup discovery: Methodology and application. Journal of Artificial Intelligence Research, 17:501-527, 2002.
15. A. D. Gordon. Classification. Chapman and Hall, London, 1982.
16. K. Chidananda Gowda and Edwin Diday. Symbolic clustering using a new dissimilarity measure. IEEE Transactions on Systems, Man, and Cybernetics, 22(2):567-578, 1992.
17. David J. Hand and Robert J. Till. A simple generalisation of the area under the roc curve for multiple class classification problems. Machine Learning, 45:171-186, 2001.
18. David Hsu, Oren Etzioni, and Stephen Soderland. A redundant covering algorithm applied to text classification. In Proceedings of the AAAI Workshop on Learning from Text Categorization, AAAI Press, 1998.
19. Viktor Jovanoski and Nada Lavrač. Classification rule learning with apriori-c. In Progress in Artificial Intelligence: Proceedings of the Tenth Portuguese Conference on Artificial Intelligence, pages 44-51, Springer, 2001.
20. Branko Kavšek, Nada Lavrač, and Viktor Jovanoski. Apriori-sd: Adapting association rule learning to subgroup discovery. In Proceedings of the Fifth International Symposium on Intelligent Data Analysis, pages 230-241, Springer, 2003.
21. Willi Klösgen. Explora: A multipattern and multistrategy discovery assistant. Advances in Knowledge Discovery and Data Mining, MIT Press: 249-271, 1996.
22. Arno J. Knobbe, Marc De Haas, and Arno Siebes. Propositionalisation and aggregates. In Proceedings of the Fifth European Conference on Principles and Practice of Knowledge Discovery in Databases, pages 277-288, Springer, 2001.
23. Arno J. Knobbe, Arno Siebes, and Bart Marseille. Involving aggregate functions in multi-relational search. In Proceedings of the Sixth European Conference on Principles and Practice of Knowledge Discovery in Databases, pages 287-298, Springer, 2002.
24. Pat Langley. Elements of Machine Learning. Morgan Kaufmann, 1996.
25. Nada Lavrač, Peter A. Flach, Branko Kavšek, and Ljupčo Todorovski. Adapting classification rule induction to subgroup discovery. In Proceedings of the Second IEEE International Conference on Data Mining, pages 266-273, IEEE Computer Society, 2002.
26. Nada Lavrač, Peter A. Flach, and Blaž Zupan. Rule evaluation measures: A unifying view. In Proceedings of the Nineth International Workshop on Inductive Logic Programming, pages 74-185, Springer, 1999.
27. Nada Lavrač, Filip Železný, and Peter A. Flach. Rsd: Relational subgroup discovery through firstorder feature construction. In Proceedings of the Twelfth International Conference on Inductive Logic Programming, pages 149-165, Springer, 2003.
28. Yongwon Lee, Bruce G. Buchanan, and John M. Aronis. Knowledge-based learning in exploratory science: Learning rules to predict rodent carcinogenicity. Machine Learning, 30:217-240, 1998.
29. Peter Ljubič, Ljupčo Todorovski, Nada Lavrač, and John C. Bullas. Time-series analysis of uk traffic accident data. In Proceedings of the Fifth International Multi-conference Information Society, pages 131-134, 2002.
30. Ryszard S. Michalski. Pattern recognition as rule-guided inductive inference. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(4):349-361, 1980.
31. Ryszard S. Michalski, Igor Mozetič, Jiarong Hong, and N. Lavrač. The multi-purpose incremental learning system aq15 and its testing application on three medical domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, pages 1041-1045, Morgan Kaufmann, 1986.
32. Patrick M. Murphy and David W. Aha. Uci repository of machine learning databases. Available electronically at http://www.ics.uci.edu/mlearn/MLRepository.html, 1994.
33. Foster J. Provost and Tom Fawcett. Robust classification for imprecise environments. Machine Learning, 42(3):203-231, 2001.
34. Luc De Raedt and Hendrik Blockeel. Using logical decision trees for clustering. In Proceedings of the Seventh International Workshop on Inductive Logic Programming, pages 133-140, Springer, 1997.
35. Luc De Raedt, Hendrik Blockeel, Luc Dehaspe, and Wim Van Laer. Three companions for data mining in first order logic. Relational Data Mining, Springer: 106-139, 2001.
36. Luc De Raedt and Luc Dehaspe. Clausal discovery. Machine Learning, 26:99-146, 1997.
37. Ronald L. Rivest. Learning decision lists. Machine Learning, 2(3):229-246, 1987.
38. Robert E. Schapire and Yoram Singer. Improved boosting algorithms using confidence-rated predictions. In Proceedings of the Eleventh Conference on Computational Learning Theory, pages 80-91, ACM Press, 1998.
39. Avi Silberschatz and Alexander Tuzhilin. On subjective measures of interestingness in knowledge discovery. In Proceedigns of the First International Conference on Knowledge Discovery and Data Mining, pages 275-281, 1995.
40. R. R. Sokal and Peter H. A. Sneath. Principles of Numerical Taxonomy. Freeman, San Francisco, 1963.
41. Ljupčo Todorovski, Peter A. Flach, and Nada Lavrač. Predictive performance of weighted relative accuracy. In Proceedings of the Fourth European Conference on Principles of Data Mining and Knowledge Discovery, pages 255-264, Springer, 2000.
42. Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 1999.
43. Stefan Wrobel. An algorithm for multi-relational discovery of subgroups. In Proceedings of the First European Conference on Principles of Data Mining and Knowledge Discovery, pages 78-87, Springer, 1997.
44. Stefan Wrobel. Inductive logic programming for knowledge discovery in databases. Relational Data Mining, Springer: 74-101, 2001.