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A Novel preprocessing Algorithm for Frequent Pattern Mining in Multidatasets
K.Duraiswamy, Jayanthi Balasubramaniam
Pages - 111 - 118 | Revised - 01-07-2011 | Published - 05-08-2011
Published in International Journal of Data Engineering (IJDE)
MORE INFORMATION
KEYWORDS
Multiple-Level, CCB-Tree, Association Rule, Minimum Support, Frequent Patterns
ABSTRACT
In many database applications, information stored in a database has a built-in hierarchy consisting of multiple levels of concepts. In such a database users may want to find out association rules among items only at the same levels. This task is called multiple-level association rule mining. However, mining frequent patterns at multiple levels may lead to the discovery of more specific and concrete knowledge from data. Initial step to find frequent pattern is to preprocess the multidataset to find the large 1 frequent pattern for all levels. In this research paper, we introduce a new algorithm, called CCB-tree i.e., Category-Content-Brand tree is developed to mine Large 1 Frequent pattern for all levels of abstraction. The proposed algorithm is a tree based structure and it first constructs the tree in CCB order for entire database and second, it searches for frequent pattern in CCB order. This method is using concept of reduced support and it reduces the time complexity.
| 1 | Ali, S. Z., & Rathore, Y. A comprehensive study of major techniques of multi level frequent pattern mining: a survey. |
| 2 | Herawan, T., Noraziah, A., Abdullah, Z., Deris, M. M., & Abawajy, J. H. (2012). EFP-M2: efficient model for mining frequent patterns in transactional database. In Computational Collective Intelligence. Technologies and Applications (pp. 29-38). Springer Berlin Heidelberg. |
| 1 | Google Scholar  |
| 2 | CiteSeerX |
| 3 | Scribd |
| 4 | SlideShare |
| 5 | PdfSR |
| Agrawal R, and Srikant R, (1994). Fast algorithms for mining association rules. In Proc. Of the 20th Int. Conf. on very Large Databases. Pages 487-499. | |
| Agrawal R,Imienlinski T,Swami A,(1993).Mining association rules between sets of items in large databases. In Proc. Of the ACM SIGMOD Int. Conf. on Management of Data, Pages 207-216. | |
| Gavin Shaw, ‘Discovery & Effective use of Quality Association Rules in Multi-Level Datasets “, Ph.D-Thesis, Queensland University of Technology, Brisbane, Australia,2010. | |
| Han .J ,Pei .J, and Yin .Y,(2000) Mining Frequent patterns without candidate generation. In Proc. Of ACM-SIGMOD Int. Conf. on Management of Data, pages 1-12. | |
| Han, J., Fu, Y., Discovery of Multiple-Level Association Rules from Large Databases, in Proceedings of the 21st Very Large Data Bases Conference, Morgan Kaufmann, P. 420- 431, 1995. | |
| Han, J., Fu, Y., Mining Multiple-Level Association Rules in Large Databases, in IEEE Transactions on Knowledge and Data Engineering, Vol. 11, No. 5, September/October 1999. | |
| Mehmet Kaya, Reda Alhajj, “ Mining Multi-Cross-Level Fuzzy Weighted Association rules”, Second IEEE International Conference on Intelligent Systems.Vol.1,pp.225-230, 2004 | |
| Mohamed Salah Gouider, Amine Farhat, “Mining Multi-level Frequent Itemsets under Constraints”, International Journal of Database Theory and Application Vol. 3, No. 4, December, 2010 | |
| Popescu, Daniela.E, Mirela Pater, “Multi-Level Database using AFOPT Data Structure and Adaptive Support Constraints”, Int. J. of Computers, Comm. & Control, Vol.3,2008. | |
| Pratima Gautham, Pardasani, K. R., “Algorithm for Efficient Multilevel Association Rule Mining”, International Journal of Computer Science and Engineering, Vol.2 pp. 1700-1704, 2010. | |
| Rajkumar.N, Karthik.M.R, Sivanada.S.N, “Fast Algorithm for mining multilevel Association Rules,”IEEE Trans. Knowledge and Data Engg., Vol.2 pp. 688-692, 2003. | |
| Synthetic Data generation Code for Associations and Sequential Patterns (IBM Almaden Research center). http://www.almaden.ibm.com/software/quest/Resources/datasets/syndata.html. | |
| Thakur, R. S., Jain, R. C., Pardasani, K. R., Mining Level-Crossing Association Rules from Large Databases, in the Journal of Computer Science 2(1), P. 76-81, 2006. | |
| Yinbo WAN, Yong LIANG, Liya DING, “Mining Multilevel Association Rules from Primitive Frequent Itemsets”, Journal of Macau University of Science and Technology, Vol.3 No.1, 2009 | |
Dr. K.Duraiswamy
- India
Associate Professor Jayanthi Balasubramaniam
Kongu Arts and Science College - India
sjaihere@gmail.com
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