Measuring influence of an item in a database over time

Pattern Recognition Letters

Volumn 31, Issue 3, 2010, Pages 179-187

  Adhikari, Jhimli ^a   Rao, P R ^b  

^a Narayan Zantye College   (India)

^b GOA UNIVERSITY   (India)

Author keywords

Confidence; Overall association; Overall influence; Synthesis of influence

Indexed keywords

DATA ANALYSIS; INCREMENTAL APPROACH; INFLUENCE ANALYSIS; REAL-WORLD DATABASE;

DATABASE SYSTEMS;

EID: 72449158358     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2009.10.010     Document Type: Article

References (21)

1. Adhikari, A., Rao, P.R., 2007a. Study of select items in multiple databases by grouping. In: Proceedings Internat. Conf. on Artificial Intelligence, pp. 1699-1718.
2. Adhikari, A., Rao, P.R., 2007b. A framework for mining arbitrary Boolean expressions induced by frequent itemsets. In: Proceedings Internat. Conf. on Artificial Intelligence, pp. 5-23.
3. Adhikari A., and Rao P.R. Efficient clustering of databases induced by local patterns. Decision Support Systems 44 4 (2008) 925-943
4. Adhikari A., and Rao P.R. Mining conditional patterns in a database. Pattern Recognition Lett. 29 10 (2008) 1515-1523
5. Adhikari A., and Rao P.R. Synthesizing heavy association rules in different real data sources. Pattern Recognition Lett. 29 1 (2008) 59-71
6. Adhikari J., Rao P.R., and Adhikari A. Clustering items in different data sources induced by stability. Internat. Arab J. Inform. Technol. 6 4 (2008) 394-402
7. Aggarwal, C., Yu, P., 1998. A new framework for itemset generation. In: Proc. PODS, pp. 18-24.
8. Agrawal, R., Imielinski, T., Swami, A., 1993. Mining association rules between sets of items in large databases. In: Proc. ACM SIGMOD Conf. on Management of Data, pp. 207-216.
9. Agrawal, R., Srikant, R., 1994. Fast algorithms for mining association rules. In: Proc. Internat. Conf. on Very Large Databases, pp. 487-499.
10. Brin, S., Motwani, R., Ullman, J.D., Tsur, S., 1997. Dynamic itemset counting and implication rules for market basket data. In: Proc. ACM SIGMOD Internat. Conf. on Management of Data, pp. 255-264.
11. Frequent itemset mining dataset repository, xxxx. .
12. Gary, J.R., Petersen, A., 2000. Analysis of cross category dependence in market basket selection. J. Retail. 76 (3), 367-392.
13. Greenwood P.E., and Nikulin M.S. A Guide to Chi-Squared Testing. first ed. (1996), Wiley-Interscience
14. Han, J., Pei, J., Yiwen, Y., 2000. Mining frequent patterns without candidate generation, In: Proc. ACM-SIGMOD Internat. Conf. on Management of Data, pp. 1-12.
15. Knuth, D.E., 1998. The Art of Computer Programming Sorting and Searching, vol. 3, second ed. Addison-Wesley Professional.
16. Savasere, A., Omiecinski, E., Navathe, S., 1995. An efficient algorithm for mining association rules in large databases. In: Proc. Internat. Conf. on Very Large Data Bases, pp. 432-443.
17. Shapiro, P., 1991. Discovery, analysis, and presentation of strong rules. Knowledge Discovery in Databases, pp. 229-248.
18. Tan, P.N., Kumar, V., Srivastava, J., 2003. Selecting the right interestingness measure for association patterns. In: Proc. SIGKDD Conference, pp. 32-41.
19. UCI ML repository, xxxx. .
20. Wu X., Zhang C., and Zhang S. Efficient mining of both positive and negative association rules. ACM Trans. Inform. Systems 22 3 (2004) 381-405
21. Wu X., Zhang C., and Zhang S. Database classification for multi-database mining. Inform. Systems 30 1 (2005) 71-88