Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining

Journal of Machine Learning Research

Volumn 10, Issue , 2009, Pages 377-403

  Novak, Petra Kralj ^a   Lavrač, Nada ^a   Webb, Geoffrey I ^b  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b MONASH UNIVERSITY   (Australia)

Author keywords

Contrast set mining; Descriptive rules; Emerging patterns; Rule learning; Subgroup discovery

Indexed keywords

DIFFUSERS (OPTICAL); EDUCATION; HEURISTIC METHODS; INFORMATION MANAGEMENT; MINING; SURVEYS; TERMINOLOGY;

CONTRAST SET MINING; DESCRIPTIVE RULES; EMERGING PATTERNS; RULE LEARNING; SUBGROUP DISCOVERY;

LEARNING ALGORITHMS;

EID: 61749084093     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (64)

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, pages 307-328, 1996.
2. Martin Atzmüller and Frank Puppe. Semi-automatic visual subgroup mining using VIKAMINE. Journal of Universal Computer Science (JUCS), Special Issue on Visual Data Mining, 11(11):
3. Martin Atzmüller and Frank Puppe. SD-Map - a fast algorithm for exhaustive subgroup discovery. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-06), pages 6-17, 2006.
4. Martin Atzmüller, Frank Puppe, and Hans-Peter Buscher. Exploiting background knowledge for knowledge-intensive subgroup discovery. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI-05), pages 647-652, 2005a.
5. Martin Atzmüller, Frank Puppe, and Hans-Peter Buscher. Profiling examiners using intelligent subgroup mining. In Proceedings of the 10th Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-05), pages 46-51, 2005b.
6. Yonatan Aumann and Yehuda Lindell. A statistical theory for quantitative association rules. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), pages 261-270, 1999.
7. Stephen D. Bay. Multivariate discretization of continuous variables for set mining. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2000), pages 315-319, 2000.
8. Stephen D. Bay and Michael J. Pazzani. Detecting group differences: Mining contrast sets. Data Mining and Knowledge Discovery, 5(3):213-246, 2001.
9. Roberto J. Bayardo. Efficiently mining long patterns from databases. In Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data (SIGMOD-98), pages 85-93, 1998.
10. Anne-Laure Boulesteix, Gerhard Tutz, and Korbinian Strimmer. A CART-based approach to discover emerging patterns in microarray data. Bioinformatics, 19(18):2465-2472, 2003.
11. Peter Clark and Tim Niblett. The CN2 induction algorithm. Machine Learning, 3(4):261-283, 1989.
12. William W. Cohen. Fast effective rule induction. In Proceedings of the 12th International Conference on Machine Learning (ICML-95), pages 115-123, 1995.
13. Olena Daly and David Taniar. Exception rules in data mining. In Encyclopedia of Information Science and Technology (II), pages 1144-1148. 2005.
14. María José del Jesus, Pedro González, Francisco Herrera, and Mikel Mesonero. Evolutionary fuzzy rule induction process for subgroup discovery: A case study in marketing. IEEE Transactions on Fuzzy Systems, 15(4):578-592, 2007.
15. Guozhu Dong and Jinyan Li. Efficient mining of emerging patterns: Discovering trends and differences. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), pages 43-52, 1999.
16. Guozhu Dong, Xiuzhen Zhang, Limsoon Wong, and Jinyan Li. CAEP: Classification by aggregating emerging patterns. In Proceedings of the 2nd International Conference on Discovery Science (DS-99), pages 30-42, 1999.
17. Hongjian Fan and Kotagiri Ramamohanara. A bayesian approach to use emerging patterns for classification. In Proceedings of the 14th Australasian Database Conference (ADC-03), pages 39-48, 2003.
18. Hongjian Fan and Kotagiri Ramamohanarao. Efficiently mining interesting emerging patterns. In Proceeding of the 4th International Conference on Web-Age Information Management (WAIM-03), pages 189-201, 2003.
19. Hongjian Fan, Ming Fan, Kotagiri Ramamohanarao, and Mengxu Liu. Further improving emerging pattern based classifiers via bagging. In Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-06), pages 91-96, 2006.
20. Jerome H. Friedman and Nicholas I. Fisher. Bump hunting in high-dimensional data. Statistics and Computing, 9(2):123-143, 1999.
21. Johannes Fürnkranz and Peter A. Flach. An analysis of rule evaluation metrics. In Proceedings of the 20th International Conference on Machine Learning (ICML-03), pages 202-209, 2003.
22. Dragan Gamberger and Nada Lavrač. Expert-guided subgroup discovery: Methodology and application. Journal of Artificial Intelligence Research, 17:501-527, 2002.
23. Dragan Gamberger, Nada Lavrač, and Dietrich Wettschereck. Subgroup visualization: A method and application in population screening. In Proceedings of the 7th International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-02), pages 31-35, 2002.
24. Gemma C. Garriga, Petra Kralj, and Nada Lavrač. Closed sets for labeled data. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-06), pages 163 - 174, 2006.
25. Robert J. Hilderman and Terry Peckham. A statistically sound alternative approach to mining contrast sets. In Proceedings of the 4th Australia Data Mining Conference (AusDM-05), pages 157-172, 2005.
26. Jože Jenkole, Petra Kralj, Nada Lavrač, and Alojzij Sluga. A data mining experiment on manufacturing shop floor data. In Proceedings of the 40th International Seminar on Manufacturing Systems (CIRP-07), 2007. 6 pages.
27. Branko Kavšek and Nada Lavrač. APRIORI-SD: Adapting association rule learning to subgroup discovery. Applied Artificial Intelligence, 20(7):543-583, 2006.
28. Willi Klösgen. Explora: A multipattern and multistrategy discovery assistant. Advances in Knowledge Discovery and Data Mining, pages 249-271, 1996.
29. Willi Klösgen and Michael May. Spatial subgroup mining integrated in an object-relational spatial database. In Proceedings of the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-02), pages 275-286, 2002.
30. Willi Klösgen, Michael May, and Jim Petch. Mining census data for spatial effects on mortality. Intelligent Data Analysis, 7(6):521-540, 2003.
31. Ron Kohavi and Foster Provost, editors. Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process, Glossary of Terms, 1998.
32. Petra Kralj, Nada Lavrač, and Blaž Zupan. Subgroup visualization. In 8th International Multiconference Information Society (IS-05), pages 228-231, 2005.
33. Petra Kralj, Ana Rotter, Nataša Toplak, Kristina Gruden, Nada Lavrač, and Gemma C. Garriga. Application of closed itemset mining for class labeled data in functional genomics. Informatica Medica Slovenica, (1):40-45, 2006.
34. Petra Kralj, Nada Lavrač, Dragan Gamberger, and Antonija Krstačić. Contrast set mining for distinguishing between similar diseases. In Proceedings of the 11th Conference on Artificial Intelligence in Medicine (AlME-07), pages 109-118, 2007a.
35. Petra Kralj, Nada Lavrač, Dragan Gamberger, and Antonija Krstačić. Contrast set mining through subgroup discovery applied to brain ischaemia data. In Proceedings of the 11th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining : (PAKDD-07), pages 579-586, 2007b.
36. Nada Lavrač, Bojan Cestnik, Dragan Gamberger, and Peter A. Flach. Decision support through subgroup discovery: Three case studies and the lessons learned. Machine Learning Special issue on Data Mining Lessons Learned, 57(1-2): 115-143, 2004a.
37. Nada Lavrač, Branko Kavšek, Peter A. Flach, and Ljupčo Todorovski. Subgroup discovery with CN2-SD. Journal of Machine Learning Research, 5:153-188, 2004b.
38. Nada Lavrač, Petra Kralj, Dragan Gamberger, and Antonija Krstačić. Supporting factors to improve the explanatory potential of contrast set mining: Analyzing brain ischaemia data. In Proceedings of the 11th Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON-07), pages 157-161, 2007.
39. Jinyan Li and Limsoon Wong. Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns. Bioinformatics, 18(10):1406-1407, 2002.
40. Jinyan Li, Guozhu Dong, and Kotagiri Ramamohanarao. Instance-based classification by emerging patterns. In Proceedings of the 14th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-2000), pages 191-200, 2000.
41. Jinyan Li, Guozhu Dong, and Kotagiri Ramamohanarao. Making use of the most expressive jumping emerging patterns for classification. Knowledge and Information Systems, 3(2):1-29, 2001.
42. Jinyan Li, Huiqing Liu, James R. Downing, Allen Eng-Juh Yeoh, and Limsoon Wong. Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients. Bioinformatics, 19(1):71-78, 2003.
43. Jessica Lin and Eamonn Keogh. Group SAX: Extending the notion of contrast sets to time series and multimedia data. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-06), pages 284-296, 2006.
44. Bing Liu, Wynne Hsu, Heng-Siew Han, and Yiyuan Xia. Mining changes for real-life applications. In Proceedings of the 2nd International Conference on Data Warehousing and Knowledge Discovery (DaWaK-2000), pages 337-346, 2000.
45. Bing Liu, Wynne Hsu, and Yiming Ma. Discovering the set of fundamental rule changes. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-01), pages 335-340, 2001.
46. Michael May and Lemonia Ragia. Spatial subgroup discovery applied to the analysis of vegetation data. In Proceedings of the 4th International Conference on Practical Aspects of Knowledge Management (PAKM-2002), pages 49-61, 2002.
47. Foster J. Provost and Tom Fawcett. Robust classification for imprecise environments. Machine Learning, 42(3):203-231, 2001.
48. J. Ross Quinlan. Induction of decision trees. Machine Learning, 1(1):81-106, 1986.
49. J. Ross Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
50. Mondelle Simeon and Robert J. Hilderman. Exploratory quantitative contrast set mining: A discretization approach. In Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Vol.2 (ICTAI-07), pages 124-131, 2007.
51. K.K.W. Siu, S.M. Butler, T. Beveridge, J.E. Gillam, C.J. Hall, A.H. Kaye, R.A. Lewis, K. Mannan, G. McLoughlin, S. Pearson, A.R. Round, E. Schultke, G.I. Webb, and S.J. Wilkinson. Identifying markers of pathology in SAXS data of malignant tissues of the brain. Nuclear Instruments and Methods in Physics Research A, 548:140-146, 2005.
52. Hee S. Song, Jae K. Kimb, and Soung H. Kima. Mining the change of customer behavior in an internet shopping mall. Expert Systems with Applications, 21(3):157-168, 2001.
53. Arnaud Soulet, Bruno Crmilleux, and Franois Rioult. Condensed representation of emerging patterns. In Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-04), pages 127-132, 2004.
54. Einoshin Suzuki. Data mining methods for discovering interesting exceptions from an unsupervised table. Journal of Universal Computer Science, 12(6):627-653, 2006.
55. Ke Wang, Senqiang Zhou, Ada W.-C. Fu, and Jeffrey X. Yu. Mining changes of classification by correspondence tracing. In Proceedings of the 3rd SIAM International Conference on Data Mining (SDM-03), pages 95-106, 2003.
56. Geoffrey I. Webb. OPUS: An efficient admissible algorithm for unordered search. Journal of Artificial Intelligence Research, 3:431-465, 1995.
57. Geoffrey I. Webb. Discovering associations with numeric variables. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-01), pages 383-388, 2001.
58. Geoffrey I. Webb. Discovering significant patterns. Machine Learning, 68(1):1-33, 2007.
59. Geoffrey I. Webb, Shane M. Butler, and Douglas Newlands. On detecting differences between groups. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-03), pages 256-265, 2003.
60. Dietrich Wettschereck. A KDDSE-independent PMML visualizer. In Proceedings of 2nd Workshop on Integration Aspects of Data Mining, Decision Support and Meta-Learning (IDDM-02), pages 150-155, 2002.
61. Tzu-Tsung Wong and Kuo-Lung Tseng. Mining negative contrast sets from data with discrete attributes. Expert Systems with Applications, 29(2):401-407, 2005.
62. Stefan Wrobel. An algorithm for multi-relational discovery of subgroups. In Proceedings of the 1st European Conference on Principles of Data Mining and Knowledge Discovery (PKDD-97), pages 78-87, 1997.
63. Stefan Wrobel. Inductive logic programming for knowledge discovery in databases. In Sašo Džeroski and Nada Lavrač, editors, Relational Data Mining, chapter 4, pages 74-101. 2001.
64. Filip Železný and Nada Lavrač. Propositionalization- based relational subgroup discovery with RSD. Machine Learning, 62:33-63, 2006.