Efficient mining of frequent item sets on large uncertain databases

IEEE Transactions on Knowledge and Data Engineering

Volumn 24, Issue 12, 2012, Pages 2170-2183

  Wang, Liang ^a   Cheung, David Wai Lok ^a   Cheng, Reynold ^a   Lee, Sau Dan ^a   Yang, Xuan S ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

approximate algorithm; Frequent item sets; incremental mining; uncertain data set

Indexed keywords

APPROXIMATE ALGORITHMS; ATTRIBUTE UNCERTAINTY; BINOMIAL DISTRIBUTION; DATA INTEGRATION; EMERGING APPLICATIONS; EXPONENTIAL NUMBERS; INCREMENTAL MINING; INCREMENTAL MINING ALGORITHMS; ITEM SETS; MINING ALGORITHMS; MINING PROCESS; POSSIBLE WORLD SEMANTICS; POSSIBLE WORLDS; SENSOR MONITORING; SYNTHETIC DATASETS; UNCERTAIN DATABASE; UNCERTAIN DATAS;

ALGORITHMS; DATA MINING; LOCATION BASED SERVICES; SEMANTICS;

DATABASE SYSTEMS;

EID: 84867943010     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2011.165     Document Type: Article

References (35)

1. A. Veloso, W. Meira Jr., M. de Carvalho, B. Pôssas, S. Parthasarathy, and M.J. Zaki, "Mining Frequent Itemsets in Evolving Databases," Proc. Second SIAM Int'l Conf. Data Mining (SDM), 2002.
2. C. Aggarwal, Y. Li, J. Wang, and J. Wang, "Frequent Pattern Mining with Uncertain Data," Proc. 15th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD), 2009.
3. C. Aggarwal and P. Yu, "A Survey of Uncertain Data Algorithms and Applications," IEEE Trans Knowledge and Data Eng., vol. 21, no. 5, pp. 609-623, May 2009.
4. R. Agrawal, T. Imielinski, and A. Swami, "Mining Association Rules between Sets of Items in Large Databases," Proc. ACM SIGMOD Int'l Conf. Management of Data, 1993.
5. O. Benjelloun, A.D. Sarma, A. Halevy, and J. Widom, "ULDBs: Databases with Uncertainty and Lineage," Proc. 32nd Int'l Conf. Very Large Data Bases (VLDB), 2006.
6. T. Bernecker, H. Kriegel, M. Renz, F. Verhein, and A. Zuefle, "Probabilistic Frequent Itemset Mining in Uncertain Databases," Proc. 15th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD), 2009.
7. C.J. van Rijsbergen, Information Retrieval. Butterworth, 1979.
8. L.L. Cam, "An Approximation Theorem for the Poisson Binomial Distribution," Pacific J. Math., vol. 10, pp. 1181-1197, 1960.
9. H. Cheng, P. Yu, and J. Han, "Approximate Frequent Itemset Mining in the Presence of Random Noise," Proc. Soft Computing for Knowledge Discovery and Data Mining, pp. 363-389, 2008.
10. R. Cheng, D. Kalashnikov, and S. Prabhakar, "Evaluating Probabilistic Queries over Imprecise Data," Proc. ACM SIGMOD Int'l Conf. Management of Data, 2003.
11. D. Cheung, J. Han, V. Ng, and C. Wong, "Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique," Proc. 12th Int'l Conf. Data Eng. (ICDE), 1996.
12. D. Cheung, S.D. Lee, and B. Kao, "A General Incremental Technique for Maintaining Discovered Association Rules," Proc. Fifth Int'l Conf. Database Systems for Advanced Applications (DASFAA), 1997.
13. W. Cheung and O.R. Zaïane, "Incremental Mining of Frequent Patterns without Candidate Generation or Support Constraint," Proc. Seventh Int'l Database Eng. and Applications Symp. (IDEAS), 2003.
14. C.K. Chui, B. Kao, and E. Hung, "Mining Frequent Itemsets from Uncertain Data," Proc. 11th Pacific-Asia Conf. Advances in Knowledge Discovery and Data Mining (PAKDD), 2007.
15. G. Cormode and M. Garofalakis, "Sketching Probabilistic Data Streams," Proc. ACM SIGMOD Int'l Conf. Management of Data, 2007.
16. N. Dalvi and D. Suciu, "Efficient Query Evaluation on Probabilistic Databases," Proc. 13th Int'l Conf. Very Large Data Bases (VLDB), 2004.
17. A. Deshpande, C. Guestrin, S. Madden, J. Hellerstein, and W. Hong, "Model-Driven Data Acquisition in Sensor Networks," Proc. 13th Int'l Conf. Very Large Data Bases (VLDB), 2004.
18. J. Han, J. Pei, and Y. Yin, "Mining Frequent Patterns without Candidate Generation," Proc. ACM SIGMOD Int'l Conf. Management of Data, 2000.
19. J. Huang, "MayBMS: A Probabilistic Database Management System," Proc. 35th ACM SIGMOD Int'l Conf. Management of Data, 2009.
20. R. Jampani, L. Perez, M. Wu, F. Xu, C. Jermaine, and P. Haas, "MCDB: A Monte Carlo Approach to Managing Uncertain Data," Proc. ACM SIGMOD Int'l Conf. Management of Data, 2008.
21. J. Ren, S.D. Lee, X. Chen, B. Kao, R. Cheng, and D.W. Cheung, "Naive Bayes Classification of Uncertain Data," Proc. IEEE Ninth Int'l Conf. Data Mining (ICDM), 2009.
22. N. Khoussainova, M. Balazinska, and D. Suciu, "Towards Correcting Input Data Errors Probabilistically Using Integrity Constraints," Proc. Fifth ACM Int'l Workshop Data Eng. for Wireless and Mobile Access (MobiDE), 2006.
23. H. Kriegel and M. Pfeifle, "Density-Based Clustering of Uncertain Data," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery in Data Mining (KDD), 2005.
24. C. Kuok, A. Fu, and M. Wong, "Mining Fuzzy Association Rules in Databases," SIGMOD Record, vol. 27, no. 1, pp. 41-46, 1998. (Pubitemid 128456025)
25. C.K.-S. Leung, Q.I. Khan, and T. Hoque, "Cantree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns," Proc. IEEE Fifth Int'l Conf. Data Mining (ICDM), 2005.
26. A. Lu, Y. Ke, J. Cheng, and W. Ng, "Mining Vague Association Rules," Proc. 12th Int'l Conf. Database Systems for Advanced Applications (DASFAA), 2007.
27. M. Mutsuzaki, "Trio-One: Layering Uncertainty and Lineage on a Conventional DBMS," Proc. Third Biennial Conf. Innovative Data Systems Research (CIDR), 2007.
28. P. Sistla, O. Wolfson, S. Chamberlain, and S. Dao, "Querying the Uncertain Position of Moving Objects," Temporal Databases: Research and Practice, Springer Verlag, 1998.
29. C. Stein, Approximate Computation of Expectations, Lecture Notes - Monograph Series, vol. 7, Inst. of Math. Statistics, 1986.
30. L. Sun, R. Cheng, D.W. Cheung, and J. Cheng, "Mining Uncertain Data with Probabilistic Guarantees," Proc. 16th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, 2010.
31. T. Jayram et al., "Avatar Information Extraction System," IEEE Data Eng. Bull., vol. 29, no. 1, pp. 40-48, Mar. 2006.
32. S. Tsang, B. Kao, K.Y. Yip, W.-S. Ho, and S.D. Lee., "Decision Trees for Uncertain Data," Proc. IEEE Int'l Conf. Data Eng. (ICDE), 2009.
33. L. Wang, R. Cheng, S.D. Lee, and D. Cheung, "Accelerating Probabilistic Frequent Itemset Mining: A Model-Based Approach," Proc. 19th ACM Int'l Conf. Information and Knowledge Management (CIKM), 2010.
34. M. Yiu, N. Mamoulis, X. Dai, Y. Tao, and M. Vaitis, "Efficient Evaluation of Probabilistic Advanced Spatial Queries on Existentially Uncertain Data," IEEE Trans Knowledge and Data Eng., vol. 21, no. 9, pp. 108-122, Jan. 2009.
35. Q. Zhang, F. Li, and K. Yi, "Finding Frequent Items in Probabilistic Data," Proc. ACM SIGMOD Int'l Conf. Management of Data, 2008.