Scalable pattern mining with Bayesian networks as background knowledge

Data Mining and Knowledge Discovery

Volumn 18, Issue 1, 2009, Pages 56-100

  Jaroszewicz, Szymon ^a   Scheffer, Tobias ^b   Simovici, Dan A ^c  

^a NATIONAL INSTITUTE OF TELECOMMUNICATIONS   (Poland)

^b MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

^c UNIVERSITY OF MASSACHUSETTS BOSTON   (United States)

Author keywords

Association rule; Background knowledge; Bayesian network; Data stream; Interestingness

Indexed keywords

APPROXIMATION ALGORITHMS; ASSOCIATION RULES; ASSOCIATIVE PROCESSING; DATA MINING; DATABASE SYSTEMS; DISTRIBUTED PARAMETER NETWORKS; FEATURE EXTRACTION; GRAPHIC METHODS; INFERENCE ENGINES; INTELLIGENT NETWORKS; KNOWLEDGE BASED SYSTEMS; METROPOLITAN AREA NETWORKS; SPEECH ANALYSIS; SPEECH RECOGNITION;

APPROXIMATION BOUNDS; BACKGROUND KNOWLEDGE; CONTROLLED EXPERIMENTS; CURRENT MODELS; DATA STREAM; EXACT ALGORITHMS; EXACT INFERENCES; GRAPHICAL MODELS; INTERACTIVE PROCESSES; INTERESTINGNESS; PATTERN MININGS; VERY LARGE DATABASES; VERY LARGE NETWORKS;

BAYESIAN NETWORKS;

EID: 57849137311     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-008-0102-5     Document Type: Article

References (50)

1. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD conference on management of data, Washington, DC, pp 207-216
2. American Heart Association (2003) Risk factors: high blood cholesterol and other lipids. http://www.americanheart.org/downloadable/heart/ 1045754065601FS13CHO3.pdf
3. Andreassen S, Jensen FV, Andersen SK, Falck B, Kjærulff U, Woldbye M, Sørensen AR, Rosenfalck A, Jensen F (1989) MUNIN-an expert EMG assistant. In: John E. Desmedt(eds) Computer-aided electromyography and expert systems, Chap 21. Elsevier Science Publishers, Amsterdam
4. Bayardo RJ, Agrawal R (1999) Mining the most interesting rules. In: Proceedings of the 5th ACM SIGKDD international conference on knowledge discovery and data mining, August 1999, pp 145-154
5. Bøttcher SG, Dethlefsen C (2003) Deal: a package for learning bayesian networks. http://www.math.auc.dk/novo/Publications/bottcher:dethlefsen: 03.ps
6. Carvalho D, Freitas A, Ebecken N (2005) Evaluating the correlation between objective rule interestingness measures and real human interest. In: 9th European conference on principles of data mining and knowledge discovery (PKDD 2005), pp 453-461
7. Cooper GF, Yoo C (1999) Causal discovery from a mixture of experimental and observational data. In: Proceedings of the fifteenth conference on uncertainty in artificial intelligence, UAI, pp 116-125
8. R Dechter 1999 Bucket elimination: a unifying framework for reasoning Arti Intell 113 1-2 41 85
9. Dong G, Li J (1999) Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the 5th International conference on knowledge discovery and data mining (KDD'99), San Diego, CA, pp 43-52
10. DuMouchel W, Pregibon D (2001) Empirical bayes screening for multi-item associations. In: Proceedings of the seventh international conference on knowledge discovery and data mining (KDD'01), pp 67-76
11. Eberhardt F, Glymour C, Scheines R (2005a) N-1 experiments suffice to determine the causal relations among n variables. Technical report, Carnegie Mellon University
12. Eberhardt F, Glymour C, Scheines R (2005b) On the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables. In: Proceedings of the 21st conference on uncertainty in artificial intelligence, UAI, pp 178-184
13. Fayyad U, Piatetski-Shapiro G, Smyth P (1996) Knowledge discovery and data mining: towards a unifying framework. In: Proceedings of the second ACM SIGKDD International conference on knowledge discovery and data mining (KDD-1996), pp 82-88
14. Gray H (1977) Gray's anatomy. Grammercy Books, New York
15. Harinarayan V, Rajaraman A, Ullman JD (1996) Implementing data cubes efficiently. In: Proceedings of the ACM SIGMOD, pp 205-216
16. Heckerman D (1995) A tutorial on learning with Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research, Redmond, WA
17. Hilderman R, Hamilton H (1999) Knowledge discovery and interestingness measures: a survey. Technical Report CS 99-04, Department of Computer Science, University of Regina
18. C Huang A Darwiche 1996 Inference in belief networks: a procedural guide Int J Approx Reason 15 3 225 263
19. Jaroszewicz S, Scheffer T (2005) Fast discovery of unexpected patterns in data, relative to a Bayesian network. In: 11th ACM SIGKDD international conference on knowledge discovery and data mining (KDD-2005), Chicago, IL, August 2005, pp 118-127
20. Jaroszewicz S, Simovici DA (2001) A general measure of rule interestingness. In: 5th European conference on principles of data mining and knowledge discovery (PKDD 2001), pp 253-265
21. Jaroszewicz S, Simovici DA (2002) Pruning redundant association rules using maximum entropy principle. In: Advances in knowledge discovery and data mining, 6th Pacific-Asia conference, PAKDD'02, Taipei, Taiwan, May 2002, pp 135-147
22. Jaroszewicz S, Simovici DA (2004) Interestingness of frequent itemsets using bayesian networks as background knowledge. In: 10th ACM SIGKDD international conference on knowledge discovery and data mining (KDD 2004), Seattle, WA, August 2004, pp 178-186
23. Jensen FV (2001) Bayesian networks and decision graphs. Springer Verlag, New York
24. GD Kleiter 1996 Propagating imprecise probabilities in bayesian networks Artif Intell 88 1-2 143 161
25. Liu B, Hsu W, Chen S (1997) Using general impressions to analyze discovered classification rules. In: Proceedings of the third international conference on knowledge discovery and data mining (KDD-97). AAAI Press, p 31
26. Liu B, Jsu W, Ma Y, Chen S (1999) Mining interesting knowledge using DM-II. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, NY, 15-18 August 1999, pp 430-434
27. Mannila H (2002) Local and global methods in data mining: basic techniques and open problems. In: ICALP 2002, 29th international colloquium on automata, languages, programming, Malaga, Spain, July 2002. Springer-Verlag
28. H Mannila H Toivonen 1997 Levelwise search and borders of theories in knowledge discovery Data Min Knowl Disc 1 3 241 258
29. Meganck S, Leray P, Manderick B (2006) Learning causal bayesian networks from observations and experiments: a decision theoretic approach. In: Proceedings of the Third International Conference on Modelling Decisions in Artificial Intelligence, MDAI, pp 58-69
30. Mitchell TM (1997) Machine learning. McGraw-Hill
31. Murphy K (1998) A brief introduction to graphical models and bayesian networks. http://www.ai.mit.edu/murphyk/Bayes/bnintro.html
32. Murphy KP (2001) Active learning of causal bayes net structure. Technical report, Department of Computer Science, UC Berkeley
33. P Myllymäki T Silander H Tirri P Uronen 2002 B-course: a web-based tool for bayesian and causal data analysis Int J Artif Intelli Tools 11 3 369 387
34. Ohsaki M, Kitaguchi S, Okamoto K, Yokoi H, Yamaguchi T (2004) Evaluation of rule interestingness measures with a clinical dataset on hepatitis. In: 8th European conference on principles of data mining and knowledge discovery (PKDD 2004), pp 362-373
35. Padmanabhan B, Tuzhilin A (1998) Belief-driven method for discovering unexpected patterns. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD'98), August 1998, pp 94-100
36. Padmanabhan B, Tuzhilin A (2000) Small is beautiful: discovering the minimal set of unexpected patterns. In: Proceedinmgs of the 6th ACM SIGKDD international conference on knowledge discovery and data mining (KDD'00), NY, August 2000, pp 54-63
37. Pearl J (1998) Probabilistic reasoning in intelligent systems. Morgan Kaufmann, Los Altos, CA
38. Pearl J (2000) Causality: models, reasoning, and inference. Cambridge University Press, Cambridge, UK
39. Shah D, Lakshmanan LVS, Ramamritham K, Sudarshan S (1999) Interestingness and pruning of mined patterns. In: 1999 ACM SIGMOD workshop on research issues in data mining and knowledge discovery
40. Silberschatz A, Tuzhilin A (1995) On subjective measures of interestingness in knowledge discovery. In: Knowledge discovery and data mining, pp 275-281
41. Smith A, Elkan C (2004) A bayesian network framework for reject inference. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (KDD-2004), pp 286-295
42. Spirtes P, Richardson T (1996) A polynomial time algorithm for determining DAG equivalence in the presence of latent variables and selection bias. In: Proceedings of the sixth international workshop on artificial intelligence and statistics
43. Spirtes P, Meek C, Richardson T (1999) An algorithm for causal inference in the presence of latent variables and selection bias. In: Glymour C, Cooper G (eds) Causation, computation and discovery, Chap. 6. MIT/AAAI Press, pp 211-252
44. Suzuki E (1997) Autonomous discovery of reliable exception rules. In: Proceedings of the third international conference on knowledge discovery and data mining (KDD-97). AAAI Press, p 259
45. Suzuki E, Kodratoff Y (1998) Discovery of surprising exception rules based on intensity of implication. In: Proceedings of PKDD-98, Nantes, France, pp 10-18
46. Tan P-N, Kumar V, Srivastava J (2002) Selecting the right interestingness measure for association patterns. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining (KDD-2002), pp 32-41
47. The TETRAD project: causal models and statistical data. http://www.phil.cmu.edu/projects/tetrad
48. Tong S, Koller D (2001) Active learning for structure in bayesian networks. In: Proceedings of the 17th international joint conference on artificial intelligence, IJCAI, pp 863-869
49. Van Allen T, Greiner R, Hooper P (2001) Bayesian error-bars for belief net inference. In: UAI '01: proceedings of the 17th conference in uncertainty in artificial intelligence, San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., pp 522-529
50. Zaki MJ (2000) Generating non-redundant association rules. In: Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining (KDD-00), NY, August 20-23 2000, pp 34-43