Data processing: artificial intelligence – Knowledge processing system
Reexamination Certificate
2011-05-31
2011-05-31
Holmes, Michael (Department: 2129)
Data processing: artificial intelligence
Knowledge processing system
Reexamination Certificate
active
07953685
ABSTRACT:
Machine readable media, methods, and computing devices are disclosed that mine a dataset using a frequent pattern array. One method of includes building a frequent pattern tree comprising a plurality of nodes to represent frequent transactions of a dataset that comprises one or more items. The method also includes transforming the frequent pattern tree to a frequent pattern array that comprises an item array and a plurality of node arrays, the item array comprising frequent transactions of the dataset and each node array to associate an item of the dataset with one or more frequent transactions of the item array. The method further includes identifying frequent transactions of the dataset based upon the frequent pattern array.
REFERENCES:
patent: 6665669 (2003-12-01), Han et al.
patent: 6941303 (2005-09-01), Perrizo
patent: 7433879 (2008-10-01), Sharma et al.
patent: 7480640 (2009-01-01), Elad et al.
patent: 7493330 (2009-02-01), Cubranic
patent: 7509677 (2009-03-01), Saurabh et al.
patent: 7698170 (2010-04-01), Darr et al.
“Optimization of Frequent Itemset Mining on Multi-Core Processor” Li Liu, Eric Li, Yimin Zhang, Zhizhong Tang, Sep. 23-28, 2007, VLDB '07: Proceedings Of the 33rd international conference on Very Large Databases.
A comparison of two suffix tree-based document clustering algorithms, Rafi, M.; Maujood, M.; Fazal, M.M.; Ali, S.M.; Information and Emerging Technologies (ICIET), 2010 International Conference on Digital Object Identifier: 10.1109/ICIET.2010.5625688 Publication Year: 2010 , pp. 1-5.
Discovery of Collocation Patterns: from Visual Words to Visual Phrases, Junsong Yuan; Ying Wu; Ming Yang; Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on Digital Object Identifier: 10.1109/CVPR.2007.383222 Publication Year: 2007 , pp. 1-8.
Automatic Pattern-Taxonomy Extraction for Web Mining, Sheng-Tang Wu; Yuefeng Li; Yue Xu; Binh Pham; Phoebe Chen; Web Intelligence, 2004. WI 2004. Proceedings. IEEE/WIC/ACM International Conference on Digital Object Identifier: 10.1109/WI.2004.10132 Publication Year: 2004, pp. 242-248.
Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays, Liu, Yunhao; Chen, Lei; Pei, Jian; Chen, Qiuxia; Zhao, Yiyang; Pervasive Computing and Communications, 2007. PerCom '07. Fifth Annual IEEE International Conference on Digital Object Identifier: 10.1109/PERCOM.2007.23 Pub Year: 2007 , pp. 37-46.
S. Shporer, “AIM2: Improved Implementation of AIM”, Proceedings of ICDM Workshop on Frequent Itemset Mining Implementations, 2004, 2 pages.
J. Park, M. Chen, and P. Yu, “An Effective Hash-Based Algorithm for Mining Association Rules”, Proceedings of the International Conference on Management of Data, 1995, 175-186, 12 pages.
A. Savasere, E. Omiecinski, and S. Navathe, “An Efficient Algorithm for Mining Association Rules in Large Database”, Proceedings of the International Conference on Very Large Data Bases, 1995, 1-24, 24 pages.
C. Lucchese, S. Orlando, P. Palmerini, R. Perego, F. Silvestri, “kDCI: a Multi-Strategy Algorithm for Mining Frequent Sets”, Proceedings of ICDM Workshop on Frequent Itemset Mining Implementations, 2003, 10 pages.
S. Brin, R. Motwani, and C. Silverstein, “Beyond Market Basket: Generalizing Association Rules to Correlations”, Proceedings of the International Conference on Management of Data, 1997, 12 pages.
A. Ghoting, G. Buehrer, S. Parthasarathy, D. Kim, A. Nguyen, Y. Chen, and P. Dubey, “Cache-Conscious Frequent Pattern Mining on a Modern Processor”, Proceedings of the International Conference on Very Large Data Bases, 2005, 577-588, 12 pages.
H. Mannila, H. Toivonen, and A.I. Verkamo, “Discovery of Frequent Episodes in Event Sequences”, Data Mining and Knowledge Discovery, 1, 3, Sep. 1997, 259-289, 31 pages.
G. Dong and J. Li, “Efficient Mining of Emerging Patterns: Discovering Trends and Differences”, Proceedings of the International Conference on Knowledge Discovery and Data Mining, 1999, 43-52, 10 pages.
J. Han, G. Dong, and Y. Yin, “Efficient Mining of Partial Periodic Patterns in Time Series Dataset”, Proceedings of the International Conference on Data Engineering, 1999, 1-10, 10 pages.
K. Gouda and M. Zaki, “Efficiently Mining Maximal Frequent Itemsets”, Proceedings of the International Conference on Data Mining, 2001, 163-170, 8 pages.
G. Grahne, J. Zhu, “Efficiently Using Prefix-Trees in Mining Frequent Itemsets”, Proceedings of ICDM Workshop on Frequent Itemset Mining Implementations, 2003, 10 pages.
R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules”, Proceedings of the International Conference on Very Large Data Bases, 1994, 487-499, 13 pages.
O. R. Zaiane, M. El-Hajj, and P. Lu, “Fast Parallel Association Rule Mining Without Candidacy Generation”, Proceedings of the International Conference on Data Mining, 2001, 665-668, 4 pages.
J. Zhou, J. Cieslewicz, K. Ross, M. Shah, Improving Dataset Performance on Simultaneous Multithreading Processors, “Proceedings of the International Conference on Very Large Data Bases”, 2005, 49-60, 12 pages.
T. Uno, M. Kiyomi, H. Arimura, “LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets”, Proceedings of ICDM Workshop on Frequent Itemset Mining Implementations, 2004, 1-11, 11 pages.
D. Burdick, M. Calimlim, and J. Gehrke, “Mafia: A Maximal Frequent Itemset Mining Algorithm for Transactional Databases”, Proceedings of the International Conference on Data Engineering, 2001, 10 pages.
R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules Between Sets of Items in Large Database”, Proceedings of the International Conference on Management of Data, 1993, 207-216, 10 pages.
J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns Without Candidate Generations”, Proceedings of the International Conference on Management of Data, 2000, 1-12, 12 pages.
R. Agrawal and R. Srikant, “Mining Sequential Patterns”, Proceedings of the International Conference on Data Engineering, 1995, 12 pages.
M. Zaki, S. S Parthasarathy, M. Ogihara, and W. Li, “New Algorithms for fast Discovery of Association Rules”, Proceedings of the International Conference on Knowledge Discovery and Data Mining, 1997, 24 pages.
B. Racz, “nonordfp: An FP-growth variation without rebuilding the FP-tree”, Proceedings of ICDM Workshop on Frequent Itemset Mining Implementations, 2004, 6 pages.
Silverstein, S. Brin, R. Motwani, and J. Ullman, “Scalable Techniques of Mining Causal Structures”, Proceedings of the International Conference on Very Large Data Bases, 1998, 594-605, 12 pages.
R. Jin, G. Yang, and G. Agrawal, “Shared Memory Parallelization of Data Mining Algorithms: Techniques, Programming Interface, and Performance”, IEEE Trans. Knowl. Data Eng. 17,1, Jan. 2005, 71-89, 19 pages.
D. Callahan, K. Kennedy, A. Porterfield, “Software Prefetching”, Proceedings of International Conference on Architectural Support for Programming Languages and Operating Systems, 1991, 40-52, 13 pages.
John W. C. Fu, Janak H. Patel, Bob L. Janssens, “Stride Directed Prefetching in Scalar Processors”, Proceedings of International Symposium on Microarchitecture, 1992, 102-110, 9 pages.
DH. Chen, CR. Lai, W Hu, WG. Chen, YM. Zhang, WM. Zheng, “Tree Partition Based Parallel Frequent Pattern Mining on Shared Memory Systems”, Proceedings of IPDPS Workshop on Parallel and Distributed Scientific and Engineering, 2006, 8 pages.
I. Pramudiono and M. Kitsuregawa, “Tree Structure Based Parallel Frequent Pattern Mining on PC Cluster”, Proceedings of the International Conference on Database and Expert System Applications, 2003, 537-547, 11 pages.
S. Orlando, P. Palmerini, R. Perego, and F. Silvestri, “An Efficient Parallel and Distributed Algorithm for Cou
Li Qiang
Liu Li
Holmes Michael
Intel Corporation
Trop Pruner & Hu P.C.
LandOfFree
Frequent pattern array does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Frequent pattern array, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Frequent pattern array will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2651457