A template model for multidimensional inter-transactional association rules

VLDB Journal

Volumn 11, Issue 2, 2002, Pages 153-175

  Feng, Ling ^a,e   Yu, Jeffrey Xu ^b   Lu, Hongjun ^c   Han, Jiawei ^d  

^a TILBURG UNIVERSITY   (Netherlands)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^c HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^d UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^e UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

Intra transactional inter transactional association rules; Multidimensional context; Template model

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; DATABASE SYSTEMS; KNOWLEDGE BASED SYSTEMS; OPTIMIZATION; SEARCH ENGINES;

INTER-TRANSACTIONAL ASSOCIATION RULES;

DATA MINING;

EID: 0036823832     PISSN: 10668888     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00778-002-0069-6     Document Type: Article

References (59)

1. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proc. ACM SIGMOD International Conference on Management of Data, pp 207-216, Washington D.C., USA
2. Agrawal R, Lin KI, Sawhney HS, Shim K (1995) Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In: Proc. 21st International Conference on Very Large Data Bases, pp 490-501, Zurich, Switzerland
3. Agrawal R, Shafer JC (1996) Parallel mining of association rules. IEEE Trans Knowl Data Eng 8(6):962-969
4. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proc. 20th International Conference on Very Large Data Bases, pp 478-499, Santiago, Chile
5. Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proc. International Conference on Data Engineering, pp 3-14, Taipei, Taiwan
6. Baralis E, Psaila G (1997) Designing templates for mining association rules. J Intell Inf Syst 9(1):7-32
7. Bettini C, Wang X, Jajodia S (1996) Testing complex temporal relationships involving multiple granularities and its applications to data mining. In: Proc. 15th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp 68-78, Montreal, Canada
8. Bettini C, Wang X, Jajodia S (1998) Mining temporal relationships with multiple granularities in time sequences. Data Eng 21(1):32-38
9. Brin S, Motwani R, Silverstein C (1997) Beyond market basket: generalizing association rules to correlations. In: Proc. ACM SIGMOD International Conference on Management of Data, pp 265-276, Tucson, Ariz., USA
10. Chakrabarti S, Sarawagi S, Dom B (1998) Mining surprising patterns using temporal description length. In: Proc. 24th International Conference on Very Large Data Bases, pp 606-617, New York, USA
11. Cheung D, Han J, Ng V, Wong CY (1996) Maintenance of discovered association rules in large databases: an incremental updating technique. In: Proc. International Conference on Data Engineering, pp 106-114, New Orleans, La., USA
12. Cheung DW, Ng VT, Fu AW, Fu YJ (1996) Efficient mining of association rules in distributed databases. IEEE Trans Knowl Data Eng 8(6):911-922
13. Cohen E, Datar M, Fujiwara S, Gionis A, Indyk P, Motwani R, Ullman JD, Yang C (2000) Finding interesting associations without support pruning. In: Proc. International Conference Data Engineering, pp 489-499, Calif., USA
14. Cryer JD (1985) Time series analysis. Duxbury, MBelmont, Calif., USA
15. Das G, Gunopulos D, Mannila H (1997) Finding similar time series. In: Proc. International Conference on Principles of Data Mining and Knowledge Discovery, pp 88-100, Norway
16. Das G, Lin KI, Mannila H, Renganathan G, Smyth P (1998) Rule discovery from time series. In: Proc. 2nd International Conference on Knowledge Discovery and Data Mining, pp 16-22, New York, USA
17. DeCoste D (1997) Mining multivariate time-series sensor data to discover behavior envelopes. In: Proc. 3rd International Conference on Knowledge Discovery and Data Mining, pp 151-154, Newport Beach, Calif., USA
18. Dehaspe L, Toivonen H (1999) Discovery of frequent DATA-LOG patterns. Data Min Knowl Discovery 3(1):7-36
19. Fang M, Shivakumar N, Garcia-Molina H, Motwani R, Ullman JD (1998) Computing iceberg queries efficiently. In: Proc. 24th International Conference on Very Large Data Bases, pp 299-310, New York, USA
20. Fukuda T, Morimoto Y, Morishita S, Tokuyama T (1996) Data mining using two-dimensional optimized association rules: schema, algorithms, and visualization. In: Proc. ACM SIGMOD International Conference on Management of Data, pp 13-23, Montreal, Canada
21. Fukuda T, Morimoto Y, Morishita S, Tokuyama T (1996) Mining optimized association rules for numeric attributes. In: Proc. 15th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp 182-191, Montreal, Canada
22. Han EH, Karypis G, Kumar V (1997) Scalable parallel data mining for association rules. In: Proc. ACM SIGMOD International Conference on Management of Data, pp 277-288, Tucson, Ariz., USA
23. Han J, Fu Y (1995) Discovery of multiple-level association rules from large databases. In: Proc. 21st International Conference on Very Large Data Bases, pp 420-431, Zurich, Switzerland
24. Han J, Fu Y (1995) Meta-rule-guided mining of association rules in relational databases. In: Proc. 1st International Workshop on Integration of Knowledge Discovery with Deductive and Object-Oriented Databases, pp 39-46, Singapore
25. Kamber M, Han J, Chiang JY (1997) Metarule-guided mining of multi-dimensional association rules using data cubes. In: Proc. International Conference on Knowledge Discovery and Data Mining, pp 207-210, Calif., USA
26. Klemettinen M, Mannila H, Ronkainen P, Toivonen H, Verkamo AI (1994) Finding interesting rules from large sets of discovered association rules. In: Proc. 3rd International Conference on Information and Knowledge Management, pp 401-408, Gaithersburg, Md., USA
27. Lakshmanan LVS, Ng R, Han J, Pang A (1999) Optimization of constrained frequent set queries with 2-variable constraints. In: Proc. ACM SIGMOD International Conference on Management of Data, pp 157-168, USA
28. Lent B, Swami A, Widom J (1997) Clustering association rules. In: Proc. International Conference on Data Engineering, pp 220-231, Birmingham, UK
29. Li CS, Yu PS, Castelli V (1996) HierachyScan: a hierachical similarity search algorithm for databases of long sequences. In: Proc. International Conference on Data Engineering, pp 546-553, New Orleans, La., USA
30. Lin L, Risch T (1998) Querying continuous time sequences. In: Proc. 24th International Conference on Very Large Data Bases, pp 170-181, New York, USA
31. Liu B, Hsu W, Ma Y (1999) Mining association rules with multiple minimum supports. In: Proc. ACM SIGKDD International Conference Knowledge Discovery and Dara Mining, pp 125-134, Calif., USA
32. Lu H, Feng L, Han J (2000) Beyond intra-transactional association analysis: mining multi-dimensional inter-transaction association rules. ACM Trans Inf Syst 18(4):423-454
33. Lu H, Han J, Feng L (1998) Stock movement prediction and n-dimensional inter-transaction association rules. In: Proc. ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, pp 12:1-12:7, Seattle, Wash., USA
34. Mannila H, Toivonen H (1996) Discovering generalized episodes using minimal occurrences. In: Proc. 2nd International Conference on Knowledge Discovery and Data Mining, pp 146-151, Portland, Ore., USA
35. Mannila H, Toivonen H, Verkamo A (1997) Discovering frequent episodes in event sequences. Data Min Knowl Discovery 3(1):259-289
36. Meo R, Psaila G, Ceri S (1996) A new SQL-like operator for mining association rules. In: Proc. 22th International Conference on Very Large Data Bases, pp 122-133, Mumbai, India
37. Miller RJ, Yang Y (1997) Association rules over interval data. In: Proc. ACM SIGMOD International Conference on Management of Data, pp 452-461, Tucson, Ariz., USA
38. Ng R, Lakshmanan LVS, Han J, Pang A (1998) Exploratory mining and pruning optimizations of constrained association rules. In: Proc. ACM SIGMOD International Conference on Management of Data, pp 13-24, Seattle, Wash., USA
39. Özden B, Ramaswamy A, Silberschatz A (1998) Cyclic association rules. In: Proc. International Conference on Data Engineering, pp 412-421, Fla., USA
40. Park JS, Chen MS, Yu PS (1995) An effective hash-based algorithm for mining association rules. In: Proc. ACM SIGMOD International Conference on Management of Data, pp 175-186, San Jose, Calif., USA
41. Park JS, Chen MS, Yu PS (1995) Mining association rules with adjustable accuracy. Technical Report IBM Research Report
42. Park JS, Chen MS, Yu PS (1996) Data mining for path traversal patterns in a web environment. In: Proc. 16th Conference on Distributed Computing Systems, pp 385-392, Hong Kong
43. Ramaswamy S, Mahajan S, Silberschatz A (1998) On the discovery of interesting patterns in association rules. In: Proc. 24th International Conference on Very Large Data Bases, pp 368-379, New York, USA
44. Rastogi R, Shim K (1998) Mining optimized association rules with categorical and numerical attributes. In: Proc. International Conference on Data Engineering, pp 503-512, Florida, USA
45. Savasere A, Omiecinski E, Navathe S (1995) An efficient algorithm for mining association rules in large databases. In: Proc. 21st International Conference on Very Large Data Bases, pp 432-443, Zurich, Switzerland
46. Silverstein C, Brin S, Motwani R, Ullman JD (1998) Scalable techniques for mining causal structures. In: Proc. 24th International Conference on Very Large Data Bases, pp 594-605, New York, USA
47. Silverstein C, Brin S, Motwani R, Ullman JD (2000) Scalable techniques for mining causal structures. Data Min Knowl Discovery 4(2/3):163-192
48. Srikant R, Agrawal R (1995) Mining generalized association rules. In: Proc. 21st International Conference on Very Large Data Bases, pp 409-419, Zurich, Switzerland
49. Srikant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. In: Proc. ACM SIGMOD International Conference on Management of Data, pp 1-12, Montreal, Canada
50. Srikant R, Vu Q, Agrawal R (1997) Mining association rules with item constraints. In: Proc. 3rd International Conference on Knowledge Discovery and Data Mining, pp 67-73, Newport Beach, Calif., USA
51. Toivonen H (1996) Sampling large databases for association rules. In: Proc. 22th Conference on Very Large Data Bases, pp 134-145, Mumbai, India
52. Tsur D, Ullman JD, Abitboul S, Clifton C, Motwani R, Nestorov S (1998) Query flocks: a generalization of association-rule mining. In: Proc. ACM SIGMOD International Conference on Management of Data, pp 1-12, Seattle, Wash., USA
53. Tung KH, Lu H, Han J, Feng L (1999) Breaking the barrier of transactions: mining inter-transaction association rules. In: Proc. ACM SIGKDD International Conference Knowledge Discovery and Data Mining, pp 297-301
54. Wang JTL, Chirn GW, Marr TG, Shapiro B, Shasha D, Zhang K (1994) Combinatorial pattern discovery for scientific data: some preliminary results. In: Proc. ACM SIGMOD International Conference on Management of Data, pp 115-125, Minn., USA
55. Wang K, He Y, Han J (2000) Mining frequent itemsets using support constraints. In: Proc. 26st International Conference Very Large Data Bases, pp 43-52, Cairo, Egypt
56. Weigend A, Gerschenfeld N (1994) Time series prediction: forecasting the future and understanding the past. Addison-Wesley, Reading, Mass., USA
57. Yi BK, Jagadish HV, Faloutsos C (1998) Efficient retrieval of similar time sequences under time warping. In: Proc. 16th International Conference on Data Engineering, pp 201-208, Fla., USA
58. Yi BK, Sidiropoulos ND, Johnson T, Jagadish HV, Faloutsos C, Biliris A (2000) Online data mining for co-evolving time sequences. In: Proc. 16th International Conference on Data Engineering, pp 13-22, Calif., USA
59. Zaki MJ, Parthasarathy S, Ogihara M, Li W (1997) New algorithms for fast discovery of association rules. In: Proc. 3rd International Conference on Knowledge Discovery, Data Mining, pp 283-286, Newport Beach, Calif., USA