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Frequent Patterns Analysis using Apriory: A Survey
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 113 - Number 12 |
| Year of Publication: 2015 |
| Authors: Madhavi G. Patil, Ravi P. Patki |
10.5120/19877-1881
|
Madhavi G. Patil, Ravi P. Patki . Frequent Patterns Analysis using Apriory: A Survey. International Journal of Computer Applications. 113, 12 ( March 2015), 13-16. DOI=10.5120/19877-1881
Abstract
In applications such as location-based services, natural habitat monitoring, web data integration, and biometric applications, the values of the underlying data are inherently noisy or imprecise. Consider a location-based application that provides querying facilities on geographical objects (e. g. , airports, vehicles, and people) extracted from satellite images. Due to the errors incurred during satellite image transmission, the locations of the geographical objects can be imprecise. The data acquired from the Global Positioning System (GPS) and remote sensors can also be inaccurate and outdated, due to measurement error and network delay. During this paper, this paper tend to propose to live pattern frequentness supported the possible world linguistics. this paper tend to establish 2 unsure sequence information models abstracted from several real-life applications involving uncertain sequence information, and formulate the matter of mining probabilistically frequent serial patterns (or p-FSPs) from information that adapt to developed models. However the amount of attainable worlds is extraordinarily giant, that makes the mining prohibitively expensive. Impressed by the renowned Pre?xSpan algorithmic program, this paper tends to develop 2 new algorithms conjointly referred to as U-Pre?xSpan.
References
- M. Muzammal and R. Raman, "Mining sequential patterns from probabilistic databases", in Proc. 15th PAKDD, Shenzhen, China, 2011
- F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi, "Trajectory pattern mining", in Proc. 13th ACM SIGKDD San Jose, CA, USA, 2007
- D. Tanasa, J. A. Lpez, and B. Trousse, "Extracting sequential patterns for gene regulatory expressions proles", in Proc. KELSI, Milan, Italy, 2004.
- J. Pei et al. , "PrexSpan: Mining sequential patterns efciently by prexprojected pattern growth", in Proc. 17th ICDE, Berlin, Germany, 2001.
- R. Agrawal and R. Srikant, "Mining sequential patterns", in Proc. 11th ICDE, Taipei, Taiwan, 1995
- M. J. Zaki, "SPADE: An efficient algorithm for mining frequent sequences", Mach. Learn. , vol. 42, no. 12, pp. 3160, 2001.
- J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. C. Hsu, "FreeSpan: Frequent pattern-projected sequential pattern mining", in Proc. 6th SIGKDD, New York, NY, USA, 2000.
- R. Srikant and R. Agrawal, "Mining sequential patterns: Generalizations and performance improvements", in Proc. 5th Int. Conf. EDBT, Avignon, France, 1996
- Z. Zhao, D. Yan, and W. Ng, "Mining probabilistically frequent sequential patterns in uncertain databases", in Proc 15th Int. Conf. EDBT, New York, NY, USA, 2012
- C. Gao and J. Wang, "Direct mining of discriminative patterns for classifying uncertain data", in Proc. 16th ACM SIGKDD, Washington, DC, USA, 2010.
- C. C. Aggarwal, Y. Li, J. Wang, and J. Wang, "Frequent pattern mining with uncertain data", in Proc. 15th ACM SIGKDD, Paris, France, 2009.
- Q. Zhang, F. Li, and K. Yi, "Finding frequent items in probabilistic data", in Proc. ACM SIGMOD, Vancouver, BC, Canada, 2008
- Nikos Pelekis, Ioannis Kopanakis, Evangelos E. Kotsifakos, Elias Frentzos "Clustering uncertain trajectories", 2010
- L. Sun, R. Cheng, D. W. Cheung, and J. Cheng, "Mining uncertain data with probabilistic guarantees," in Proc. 16th ACM SIGKDD, Washington, DC, USA, 2010.
- C. C. Aggarwal, Y. Li, J. Wang, and J. Wang, "Frequent pattern mining with uncertain data," in Proc. 15th ACM SIGKDD, Paris, France, 2009.
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