The week's pick
Reseach Article
A New Approach for Extracting Closed Frequent Patterns and their Association Rules using Compressed Data Structure
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 72 - Number 9 |
| Year of Publication: 2013 |
| Authors: Vimal Kishor Tiwari, Anju Singh |
10.5120/12519-6809
|
Vimal Kishor Tiwari, Anju Singh . A New Approach for Extracting Closed Frequent Patterns and their Association Rules using Compressed Data Structure. International Journal of Computer Applications. 72, 9 ( June 2013), 1-7. DOI=10.5120/12519-6809
Abstract
In data mining, term frequent pattern extraction is largely used for finding out association rules. Generally association rule mining approaches are used as bottom-up or top-down approach on compressed data structure. In the past, different works proposed different approaches to mine frequent patterns from giving databases. In this paper, we propose a new approach by applying the closed & intersection approach using compressed data structure. We have used closed as bottom-up and intersection as top-down approach. This combined approach allows diminishing the search time by reducing database scan for finding out closed frequent patterns and their association rules. The time complexity of the proposed algorithm is less while the classical approach like a priori has taken more time for given items in the dataset. Experimental results show that our approach is more efficient and effective than a traditional apriori algorithm.
References
- Xiaobing Liu, Kun Zhai, Witold Pedrycz, "An improved association rules mining method", Pages 1362-1374, ACM 2012.
- Xin Zhang, Pin Liao, Huiyong Wang, "A New Association Rules Mining Algorithm", Page(s): 429 – 432, IEEE 2009.
- Xain-hui Chang & Don-Lin Yang "Efficient mining of maximal frequent itemsets with the closed & intersection approach", Department of Information Engineering & Computer Science,Feng Chia University Taiwan.
- Panida Songram and Veera Boonjing "Closed multidimensional sequential pattern mining", International Journal Knowledge Management Studies, Vol. 2, No. 4, 2008. pp. 460 – 479
- Qian Wan and Aijun "Compact Transaction Database for Efficient Frequent Pattern Mining" An Department of Computer Science and Engineering York University, Toronto, Ontario, M3J 1P3, Canada.
- Agrawal R. , Imielinski T. , and Swami A. N. , "Mining association rules between sets of items in large databases", In SIGMOD Conference, pages 207–216, 1993.
- Hu T. , Sung S. Y. , Xiong H. , and Fu Q. , "Discovery of maximum length frequent itemsets". Inf. Sci. , 178:69–87, January 2008.
- Quang T. M. , Oyanagi S. , and Yamazaki K. , "Mining the k-most interesting frequent patterns sequentially". In E. Corchado, H. Yin, V. J. Botti, and C. Fyfe, editors, IDEAL, volume 4224 of Lecture Notes in Computer Science, pages 620–628. Springer, 2006.
- Kumar, R. ; Dixit, M. , Analysis on probabilistic and binary datasets through frequent itemset mining, Page(s): 263 - 267 Conference Publications, IEEE 2012.
- Schmidt Jana and Kramer Stefan , "The Augmented Itemset Tree: A Data Structure for Online Maximum Frequent Pattern Mining", pp 277-291 Springer 2011.
- Tanbeer S. K. , Ahmed C. F. , Jeong B. -S. , and Lee Y. -K. , "Discovering periodic-frequent patterns in transactional databases". In PAKDD '09: Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pages 242–253, Berlin, Heidelberg, Springer-Verlag 2009.
- Ceglar A. and Roddick J. F. , "Association mining. ACM Computing", Survey, 38, July 2006.
- Han J. , Cheng H. , Xin D. , and Yan X. , "Frequent pattern mining: Current status and future directions", Data Mining and Knowledge Discovery, 14(1), 2007.
- Stankovic, S. V. , Rakocevic, G. , "A classification and comparison of Data Mining algorithms for Wireless Sensor Networks", Page: 265 – 270, IEEE 2011.
- Clifton C. , "Encyclopedia britannica: Definition of data mining", 2010.
- Yudho Giri Sucahyo Raj P. Gopalan CT-ITL: Efficient Frequent Item Set Mining Using a Compressed Prefix Tree with Pattern Growth.
- Haiying Ma,Dong Gang, "Generalized association rules and decision tree", Page(s) 4600 – 4603, IEEE, 2011.
- Kim Hyea Kyeong, Kim Jae Kyeong , "A product network analysis for extending the market basket analysis", Pages 7403–7410, Elsevier 2012.
- Brijs T. , Swinnen G. , Vanhoof K. , and Wets G. , "Using association rules for product assortment decisions: A case study", In KDD, pages 254–260, 1999.
- Yudho Giri, Sucahyo Raj, P. Gopalan, CT-PRO: A Bottom-Up Non Recursive Frequent Itemset Mining Algorithm Using Compressed FP-Tree Data Structure.
- Anthony K. H. Tung, Hongjun Lu, Jiawei Han and and Ling Feng, "Efficient Mining of Intertransaction Association Rules", IEEE Transaction on knowledge and data engineering, VOL. 15, NO. 1, pages 46-56, 2003.
- Vicente Baez-Monroy and Simon O'Keefe, "An Associative Memory for Association Rule Mining", IEEE Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, pages 2227-2232, 2007.
- Zhang Hui, LuYu and Zhou jinshu, "Study on Association Rules Mining Based on Searching Frequent Free Item Sets using Partition", Asia-Pacific Conference on Information Processing, pages 343-346,2009.
- Weimin Ouyan gand Qinhua Huang, "Mining Direct and Indirect Association Patterns with Multiple Minimum Supports", Computational Intelligence and Software Engineering (CISE), International Conference on IEEE, pages 1-4, 2010.
- Sarjon Defit, "Intelligent Mining Association Rules", International Journal of Computer Science & Information Technology (IJCSIT), Vol. 4, No 4, pages 97-106, 2012.
- Wim Le Page "Mining Patterns in Relational Databases", Universiteit Antwerpen, pages 1-21, 2009.
- Fayyad Usama, "Advances in Knowledge Discovery and Data Mining". Cambridge:MIT Press, 1996.
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