The week's pick
Random Articles
Reseach Article
Frequent Itemsets Mining on Large Uncertain Databases: Using Rule Mining Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 65 - Number 20 |
| Year of Publication: 2013 |
| Authors: Jency Varghese, K.soundararajan |
10.5120/11038-6181
|
Jency Varghese, K.soundararajan . Frequent Itemsets Mining on Large Uncertain Databases: Using Rule Mining Algorithm. International Journal of Computer Applications. 65, 20 ( March 2013), 6-10. DOI=10.5120/11038-6181
Abstract
In recent years, due to the wide applications of uncertain data mining frequent item sets over uncertain databases has attracted much attention. In uncertain databases, the support of an item set is a random variable instead of a fixed occurrence counting of this itemset. The data manipulated from sensor monitoring system and data integration diligence is highly ambiguous. One of the major issues is extracting frequent itemsets from a large uncertain database, interpreted under the Possible World Semantics. An uncertain database contains an exponential number of possible worlds, by observing that the mining process can be modeled as a Poisson binomial distribution. Mining such manifold Itemsets from generous ambiguous database illustrated under possible world semantics is a crucial dispute. Approximated algorithm is established to ascertain manifold Itemsets from generous ambiguous database exceedingly. This paper proposes Rule mining algorithm, which enable probabilistic frequent itemset results to be refreshed incase of update, delete and insert operations and also criticize the support for incremental mining and ascertainment of manifold Itemsets. Tuple and Attribute ambiguity is reinforced. Incremental Mining Algorithm is adduced to retain the mining consequence.
References
- Liang Wang, David W. Cheung, Reynold Cheng, Sau Dan Lee and Xuan Yang. Efficient Mining of Frequent Itemsets on Large Uncertain Databases. In IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2011.
- C. Aggarwal and P. Yu. A survey of uncertain data algorithms and applications. TKDE, 21(5), 2009.
- Adriano Veloso and Wagner Meira Jr. and M´arcio de Carvalho and Bruno Pˆossas and Srinivasan Parthasarathy and Mohammed Javeed Zaki. Mining Frequent Itemsets in Evolving Databases. In SDM, 2002.
- C. Aggarwal, Y. Li, J. Wang, and J. Wang. Frequent pattern mining with uncertain data. In KDD, 2009.
- R. Agrawal, T. Imieli ´ nski, and A. Swami. Mining association rules between sets of items in large databases. In SIGMOD, 1993.
- O. Benjelloun, A. D. Sarma, A. Halevy, and J. Widom. ULDBs: Databases with uncertainty and lineage. In VLDB, 2006.
- C. K. Chui, B. Kao, and E. Hung. Mining frequent itemsets from Uncertain data. In PAKDD, 2007.
- T. Bernecker, H. Kriegel, M. Renz, F. Verhein, and A. Zuefle. Probabilistic frequent itemset mining in uncertain databases. In KDD, 2009.
- C. J. van Rijsbergen. Information Retrieval. Butterworth, 1979.
- L. L. Cam. An approximation theorem for the Poisson binomial Distribution. In Pacific Journal of Mathematics, volume 10, 1960.
- H. Cheng, P. Yu, and J. Han. Approximate frequent itemset mining in the presence of random noise. Soft Computing for Knowledge Discovery and Data Mining, pages 363–389, 2008.
- R. Cheng, D. Kalashnikov, and S. Prabhakar. Evaluating probabilistic queries over imprecise data. In SIGMOD, 2003.
- D. Cheung, J. Han, V. Ng, and C. Wong. Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. In ICDE, 1996.
- D. Cheung, S. D. Lee, and B. Kao. A General Incremental Technique for Maintaining Discovered Association Rules. In DASFAA, 1997.
- W. Cheung and O. R. Za¨?ane. Incremental mining of frequent patterns without candidate generation or support constraint. In IDEAS, 2003.
- G. Cormode and M. Garofalakis. Sketching probabilistic data streams. In SIGMOD, 2007.
- N. Dalvi and D. Suciu. Efficient query evaluation on probabilistic databases. In VLDB, 2004.
- A. Deshpande, C. Guestrin, S. Madden, J. Hellerstein, and W. Hong. Model-driven data acquisition in sensor networks. In VLDB, 2004.
- C. Kuok, A. Fu, and M. Wong. Mining fuzzy association rules in databases. SIGMOD Record, 27(1):41–46, 1998.
- J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate Generation. In SIGMOD, 2000.
- J. Huang, L. Antova, C. Koch and D. Olteanu (2009), 'MayBMS: A Probabilistic Database Management System', In SIGMOD.
- R. Jampani, L. Perez, M. Wu, F. Xu, C. Jermaine, and P. Haas, (2008), 'MCDB: A Monte Carlo Approach to Managing Uncertain Data', In SIGMOD.
- A. Lu, Y. Ke, J. Cheng, and W. Ng. Mining vague association rules. In DASFAA, 2007.
- H. Kriegel and M. Pfeifle. Density-based clustering of uncertain data. In KDD, 2005.
- Q. Zhang, F. Li, and K. Yi. Finding frequent items in probabilistic data. In SIGMOD, 2008.
Index Terms
Keywords