Interestingness of association rules in data mining: Issues relevant to e-commerce

Sadhana - Academy Proceedings in Engineering Sciences

Volumn 30, Issue 2-3, 2005, Pages 291-309

  Natarajan, Rajesh ^a   Shekar, B ^b  

^a INDIAN INSTITUTE OF MANAGEMENT   (India)

^b INDIAN INSTITUTE OF MANAGEMENT BANGALORE   (India)

Author keywords

Association rules; Data mining; Interestingness measures; Post processing; Subjective objective interestingness

Indexed keywords

ALGORITHMS; CONSTRAINT THEORY; ELECTRONIC COMMERCE; INTERNET; MATHEMATICAL MODELS; PURCHASING; SERVERS;

ASSOCIATION RULES; INTERESTINGNESS MEASURES; POST-PROCESSING METHODS; SUBJECTIVE/OBJECTIVE INTERESTINGNESS;

DATA MINING;

EID: 23044453906     PISSN: 02562499     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF02706249     Document Type: Article

References (66)

1. Adomavicius G, Tuzhilin A 2001 Expert-driven validation of rule-based user models in personalization applications. Data Mining Knowledge Discovery 5(1/2): 33-58
2. Adomavicius G, Tuzhilin A 1997 Discovery of actionable patterns in databases: the action hierarchy approach. Proc. Third Int. Conf. on Data Mining and Knowledge Discovery (KDD 1997) (Meulo Park, CA: AAAI Press) pp 111-114
3. Aggarwal C C, Yu P S 2001 Mining associations with the collective strength approach. IEEE Trans. Knowledge Data Eng. 13: 863-873
4. Agrawal R, Imielinski T, Swami A 1993 Mining association rules between sets of items in large databases. Proc. 1993 ACM SIGMOD Int. Conf. on Management of Data (Washington, DC: ACM Press) pp 207-216
5. Anderberg M R 1973 Cluster analysis for applications (New York: Academic Press)
6. Ansari S, Kohavi R, Mason L, Zhang Z 2000 Integrating e-commerce and data mining: Architecture and challenges. Proc. WEBKDD'2000 Workshop: Web mining for e-commerce Challenges and Opportunities; http://ai.stanford.edu/∼ronnyk/ WEBKDD2000/index.html
7. Baesens B, Viaene S, Vanthienen J 2000 Post-processing of association rules. Proc. Workshop on Post-Processing in Machine Learning and Data Mining: Interpretation, visualization, integration, and related topics with in Sixth ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2000) Boston, MA, pp 20-23, http://www.cas.mcmaster.ca/∼bruha/kdd2000/kddrep.html
8. Baeza-Yates R, Ribeiro-Neto B 1999 Modern information retrieval (Reading, MA: Addison Wesley)
9. Bayardo Jr R J, Agrawal R, Gunopulos D 2000 Constraint-based rule mining in large, dense databases. Data Mining Knowledge Discovery 4(2/3): 217-240
10. Brieman L, Friedman J H, Olshen R, Stone C 1984 Classification and regression trees (Belmont, CA: Wadsworth)
11. Brin S, Motwani R, Silverstein C 1997a Beyond market baskets: Generalizing association rules to correlations. Proc. ACM SIGMOD Conf. (New York: ACM Press) pp 265-276
12. Brin S, Motwani R, Ullman J D, Tsur S 1997b Dynamic itemset counting and implication rules for market basket data. Proc. ACM SIGMOD Conf. (New York: ACM Press) pp 255-264
13. Burt S, Sparks L 2003 e-Commerce and the retail process: A review. J. Retailing Customer Services 10: 275-286
14. Cristofer L, Simovici D 2002 Generating an informative cover for association rules. Proc. 2002 IEEE Int. Conf. on Data Mining (ICDM 2002) (Washington, DC: IEEE Comput. Soc. Press) pp 597-600
15. Deo N 1989 Graph theory with applications to engineering and computer science. (New Delhi: Prentice Hall of India)
16. Fayyad U, Uthurusamy R 2002 Evolving data mining into solutions for insights. Commun. ACM 45(8): 28-31
17. Fayyad U M, Piatetsky-Shapiro G, Smyth P 1996 From data mining to knowledge discovery: An overview. In Advances in knowledge discovery and data mining (eds) U M Fayyad, G Piatetsky-Shapiro, P Smyth, R Uthurusamy (AAAI/MIT Press) pp 1-34
18. Freitas A A 1998 On objective measures of rule surprisingness. Proc. Second European Symposium on Principles of Data Mining and Knowledge Discovery, (PKDD-98), Lecture Notes in Artificial Intelligence. (LNAI 1510), (Springer-Verlag) pp 1-9
19. Freitas A A 1999 On rule interestingness measures. Knowledge-Based Syst. 12: 309-315
20. Geoffrion A M, Krishnan R 2003a e-Business and management science: Mutual impacts (Part 1). Manage. Sci. 49: 1275-1286.
21. Geoffrion A M, Krishnan R 2003b e-Business and management science: Mutual impacts (Part 2). Manage. Sci. 49: 1445-1456
22. Geyer-Schulz A, Hahsler M 2002 Comparing two recommender algorithms with the help of recommendations by peers. In WEBKDD 2002 - Mining web data for discovering usage patterns and profiles: 4th Int. Workshop, (eds) O R Zaiane, J Srivastava, M Spiliopoulou, B Masand (Revised Papers) Lecture Notes in Computer Science LNAI 2703 (Berlin: Springer-Verlag) pp 137-158
23. Grabmeier J, Rudolph A 2002 Techniques of cluster algorithms in data mining. Data Mining Knowledge Discovery 6: 303-360
24. Gupta G K, Strehl A, Ghosh J 1999 Distance based clustering of association rules. Proc. Intelligent Engineering Systems through Artificial Neural Networks (ANNIE 1999) (St. Louis, MO: ASME Press) vol. 9, pp 759-764
25. Hilderman R J, Hamilton H J 1999 Knowledge discovery and interestingness measures: A survey. Technical Report, Department of Computer Science, University of Regina, Canada
26. Hilderman R J, Li L, Hamilton H J 2002 Visualizing data mining results with domain generalization graphs. In Information visualization in data mining and knowledge discovery (eds) U M Fayyad, G G Grinstein, A Wierse Andreas (San Franscisco, CA: Morgan Kaufman) pp 251-270
27. Hussain F, Liu H, Suzuki E, Lu H 2000 Exception rule mining with a relative interestingness measure. Proc. Pacific Asia Conf. on Knowledge Discovery in Databases (PAKDD 2000) (London: Springer Verlag) pp 86-97
28. Jain A K, Murty M N, Flynn P J 1999 Data clustering: A review. ACM Comput. Surv. 31(3): 264-323
29. Jeh G, Widom J 2002 SimRank: A measure of structural-context similarity. Proc. 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD-2002
30. Jorge A 2004 Hierarchical clustering for thematic browsing and summarization of large sets of association rules. Proc. 2004 SIAM Int. Conf. on Data Mining (SDM 2004) (SIAM Press)
31. Kalakota R, Whinston A B 1999 Frontiers of electronic commerce (Singapore: Addison Wesley/Longman)
32. Kaufman L, Rousseeuw P J 1990 Finding groups in data: An introduction to cluster analysis (New York: Wiley)
33. Klemettinen M, Mannila H, Ronkainen P, Toivonen H, Verkamo I A 1994 Finding interesting rules from large sets of discovered association rules. Proc. Third Int. Conf. on Information and Knowledge Management (CIKM 1994) (ACM Press) pp 401-407
34. Kohavi R, Provost F 2001 Applications of data mining to electronic commerce. Data Mining Knowledge Discovery 5(1/2): 5-10
35. Korn F, Labrinidis A, Kotidis Y, Faloutos C 1998 Ratio rules: A new paradigm for fast, quantifiable data mining. Proc. 24th Int. Conf. on Very Large Databases (VLDB 1998) (New York: Morgan Kaufmann) pp 582-593
36. Lee J, Podlaseck M, Schonberg E, Hoch R 2001 Visualization and analysis of clickstream data of online stores for understanding web merchandising. Data Mining Knowledge Discovery 5(1/2): 59-84
37. Liu B, Hsu W, Mun L, Lee H 1999 Finding interesting patterns using user expectations. IEEE Trans. Knowledge Data Eng. 11: 817-832
38. Liu B, Hu M, Hsu W 2000 Multi-level organization and summarization of the discovered rules. Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2000) (Boston: ACM Press) pp 208-217
39. Lu H, Feng L, Han J 2000 Beyond intra-transaction association analysis: Mining multi-dimensional inter-transaction association rules. ACM Trans. Inf. Syst. 18(4): 423-454
40. Major J A, Mangano J J 1995 Selecting among rules induced from a hurricane database. J. Intell. Inf. Syst. 4: 39-52
41. Matheus C, Piatetsky-Shapiro G, McNeill D 1996 Selecting and reporting what is interesting: The KEFIR application to healthcare data. In Advances in knowledge discovery and data mining (eds) U M Fayyad, G Piatetsky-Shapiro, P Smyth, R Uthurusamy (Menlo Park, CA: AAAI/MIT Press) pp 495-516
42. Meo R 2000 Theory of dependence values. ACM Trans. Database Syst. 25: 380-406
43. Murthi B P S, Sarkar S 2003 The role of the management sciences in research on personalization. Manage. Sci. 49: 1344-1362
44. Ozden B, Sridhar R, Silberschatz A1998 Cyclic association rules. Proc. Fourteenth Int. Conf. on Data Engineering (ICDE 1998) (Washington, DC: IEEE Comput. Soc. Press) pp 412-421
45. Padmanabhan B, Tuzhilin A 1999 Unexpectedness as a measure of interestingness in knowledge discovery. decision support syst. 27: 303-318
46. Page L, Brin S, Motwani R, Winograd T 1998 The PageRank citation ranking: Bringing order to the web. Technical report, Stanford University Database Group. http//citeseer.nj.nec.com/368196.html
47. Piatetsky-Shapiro G, Steingold S 2000 Measuring lift quality in database marketing. ACM SIGKDD Explorations Newslett. 2(2): 76-80
48. Quinlan J 1993 C4.5: Programs for machine learning (New York: Morgan Kaufmann)
49. Ram A 1990 Knowledge goals: A theory of interestingness. Proc. 12th Annual Conf. of the Cognitive Science Society, Cambridge, MA
50. Resnick P, Varian H R 1997 Recommender systems. Commun. ACM 40(3): 56-58
51. Roddick J F, Rice S 2001 What's interesting about cricket? - On thresholds and anticipation in discovered rules. ACM SIGKDD Explorations Newslett. 3(1): 1-5
52. Sahar S 1999 Interestingness via what is not interesting. Proc. ACM Conf. on Data Mining (KDD-99) (San Diego, CA: ACM Press) pp 332-336
53. Sahar S 2002 Exploring interestingness through clustering: A framework. Proc. IEEE Int. Conf. on Data Mining (ICDM 2002) (Washington, DC: IEEE Comput. Soc. Press) pp 677-680
54. Sarwar B, Karypis G, Konstan J, Riedl J 2000 Analysis of recommendation algorithms for e-commerce. Proc. e-Commerce 2000 (New York: ACM Press) pp 158-167
55. Savasere A, Omiecinski E, Navathe S 1998 Mining for strong negative associations in a large database of customer transactions. Proc. Fourteenth Int. Conf. on Data Engineering (ICDE 1998) (Washington, DC: IEEE Comput. Soc. Press) pp 494-502
56. Schafer J B, Konstan J A, Riedl J 2001 e-Commerce recommendation applications. Data Mining Knowledge Discovery 5(1/2): 115-153
57. Shekar B, Natarajan R 2004a A framework for evaluating knowledge-based interestingness of association rules. Fuzzy Optim. Decision Making 3(2): 157-185
58. Shekar B, Natarajan R 2004b A transaction-based neighbourhood-driven approach to quantifying interestingness of association rules. Proc. Fourth IEEE Int. Conf. on Data Mining (ICDM 2004) (Washington, DC: IEEE Comput. Soc. Press) pp 194-201
59. Silberschatz A, Tuzhilin A 1996 What makes patterns interesting in knowledge discovery systems. IEEE Trans. Knowledge Data Eng. 8: 970-974
60. Srikant R, Agrawal R 1995 Mining generalized association rules. Proc. 21st Int. Conf on Very Large Databases (VLDB 1995) (New York: Morgan Kaufmann)
61. Subramanian D K, Ananthanarayana V S, Narasimha Murty M 2003 Knowledge-based association rule mining using AND-OR taxonomies. Knowledge-Based Syst. 16: 37-45
62. Tan P, Kumar V, Srivastava J 2004 Selecting the right interestingness measure for association patterns. Inf. Syst. 29(4): 293-331
63. Teng W, Hsieh M, Chen M 2002 On the mining of substitution rules for statistically dependent items. Proc. IEEE Int. Conf. on Data Mining (ICDM 2002) (Washington, DC: IEEE Comput. Soc. Press) pp 442-449
64. Toivonen H, Klemettinen M, Ronkainen P, Hatonen K, Mannila H 1995 Pruning and grouping discovered association rules. Proc. Mlnet Workshop on Statistics, Machine Learning and Knowledge Discovery in Databases, Herakhion, Crete, Greece
65. Wang K, Tay Soon H W, Liu B 1998 Interestingness-based interval merger for numeric association rules. Proc. 4th Int. Conf. on Data Mining and Knowledge Discovery (KDD 98) (New York: AAAI Press) pp 121-128
66. Zaki M J 2000 Generating non-redundant association rules. Proc. 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2000) (New York: ACM Press) pp 34-43