Mining globally interesting patterns from multiple databases using kernel estimation

Expert Systems with Applications

Volumn 36, Issue 8, 2009, Pages 10863-10869

  Zhang, Shichao ^a,b   You, Xiaofang ^a   Jin, Zhi ^c   Wu, Xindong ^d,e  

^a ZHEJIANG NORMAL UNIVERSITY   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c PEKING UNIVERSITY   (China)

^d HEFEI UNIVERSITY OF TECHNOLOGY   (China)

^e University of Vermont   (United States)

Author keywords

Global pattern; Multiple data source discovery; Multiple database mining

Indexed keywords

CUSTOMER MANAGEMENT SYSTEMS; DECISION MAKERS; GLOBAL PATTERN; GLOBAL PATTERNS; KERNEL ESTIMATION; LOCAL INFORMATION; LOCAL PATTERNS; MULTI-DATABASE MINING; MULTIPLE DATA SOURCE DISCOVERY; MULTIPLE DATABASE MINING; NON-LINEAR METHODS; PRIVACY CONCERNS; SPACE COMPLEXITY;

COMPUTER PRIVACY; ESTIMATION;

MINING;

EID: 67349246439     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.01.030     Document Type: Article

References (23)

1. Agrawal, D., & Aggarwal, C. (2001). On the design and quantification of privacy preserving data mining algorithm. In PODS (pp. 247-255).
2. Agrawal R., Imielinski T., and Swami A. Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering 5 (1993) 914-925
3. Aronis, J. et al. (1997). The WoRLD: Knowledge discovery from multiple distributed databases. In Proceedings of 10th international Florida AI research symposium (pp. 337-341).
4. Berger P., and Nasr N. Customer lifetime value: Marketing models and applications. Journal of Interactive Marketing 12 1 (1998) 17-30
5. Chattratichat, J. et al. (1997). Large scale data mining: Challenges and responses. In KDD (pp. 143-146).
6. Cheng P. Nonparametric estimation of mean functionals with data missing at random. Journal of American Statistics Association 89 (1994) 81-87
7. Cheung D., Ng V., Fu A., and Fu Y. Efficient mining of association rules in distributed databases. IEEE Transactions on Knowledge and Data Engineering 8 (1996) 911-922
8. Galindo C. On nonparametric estimation of mean functionals. Statistics and Probability Letters 39 (1998) 143-149
9. Gelbrich, K., & Nakhaeizadeh, R. (2000). Value miner: A data mining environment for the calculation of the customer lifetime value with application to the automotive industry. In ECML (pp. 154-161).
10. Ling, C., & Li, C. (1998). Data mining for direct marketing: Problems and solutions. In KDD (pp. 73-79).
11. Liu, H., Lu, H., & Yao, J. (1998). Identifying relevant databases for multidatabase mining. In PAKDD (pp. 210-221).
12. Pan, J., Kwok, J., Yang, Q., & Chen, Y. (2005). Accurate and low-cost location estimation using kernels. In IJCAI (pp. 1366-1371).
13. Park, J., Chen, M., & Yu, P. (1995). Efficient parallel and data mining for association rules. In Proceedings of CIKM (pp. 31-36).
14. Prodromidis A., Chan P., and Stolfo S. Meta-learning in distributed data mining systems: Issues and approaches. In: Kargupta H., and Chan P. (Eds). Advances in distributed and parallel knowledge discovery (2000), AAAI/MIT Press 81-114
15. Reichheld F. The loyalty effect - The relationship between loyalty and profits. Journal of European Business Journal 12 3 (2000) 173-179
16. Ribeiro, J., Kaufman, K., & Kerschberg, L. (1995). Knowledge discovery from multiple databases. In KDD (pp. 240-245).
17. Shintani, T., & Kitsuregawa, M. (1998). Parallel mining algorithms for generalized association rules with classification hierarchy. In SIGMOD (pp. 25-36).
18. Vaidya, J., & Clifton, C. (2002). Privacy preserving association rule mining in vertically partitioned data. In KDD (pp. 6390-6644).
19. Wrobel, S. (1997). An algorithm for multi-relational discovery of subgroups. In PKDD (pp. 367-375).
20. Wu X., and Zhang S. Synthesizing high-frequency rules from different data sources. IEEE Transactions on Knowledge and Data Engineering 15 (2003) 353-367
21. Zhang, C., & Zhang, S. (2002). Database clustering for mining multi-databases. In Proceedings of the 11th IEEE international conference on fuzzy systems, Honolulu, Hawaii, USA (pp. 12-17), May.
22. Zhong N., Yao Y., and Ohshima M. Peculiarity oriented multi-database mining. IEEE Transactions on Knowledge and Data Engineering 15 (2003) 952-960
23. Zhang S., Zhang C., and Yu J. An efficient strategy for mining exceptions in multi-databases. Information Sciences 165 (2004) 1-20