|
| Discovery of Frequent Itemsets based on Minimum Quantity and Support
|
|
Full
text: |
 PDF(119.6KB) |
|
|
Source |
International Journal of Computer Science and Security (IJCSS) |
|
Table of Contents |
|
|
Download
Complete Issue PDF(3.33MB) |
|
Volume: 3 Issue: 3 |
| |
Pages: 154-271 |
|
Publication
Date: June 2009 |
|
ISSN
(Online): 1985-1553 |
|
|
|
|
|
Pages |
216 - 225 |
|
Author(s) |
|
|
|
Published
Date |
01-09-2009 |
|
Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
|
ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
| |
|
| |
KEYWORDS: Channel allocation problem, tabu search, simulated annealing |
|
|
| |
|
|
| This Manuscript is indexed in the following databases/websites:- |
|
| 1. Directory of Open Access Journals (DOAJ) |
| 2. Free-Books-Online |
| 3. PDFCAST |
| 4. Scribd |
| 5. Docstoc |
| 6. Google Scholar |
| 7. CiteSeerX |
| 8. ScientificCommons |
| 9. WorldCat |
| 10. Bielefeld Academic Search Engine (BASE) |
| 11. ResearchGATE |
| 12. Academic Index |
| 13. Socol@r |
| 14. iSEEK |
| 15. Microsoft Academic Search |
| 16. Academic Journals Database |
| 17. Libsearch |
| 18. slideshare |
| 19. Chinese Directory Of Open Access |
| |
|
| |
|
|
| Most of the association rules mining algorithms to discover frequent itemsets do not consider the components of transactions such as number of items bought or quantity and its total cost. In a large database it is possible that even if the itemset appears in a very few transactions, it may be purchased in a large quantity for every transaction in which it is present, and may lead to very high profit. Therefore the quantity and its total cost of the item bought are the most important components, without which it may lead to loss of information. Our novel method discovers all frequent itemsets based on items quantity, in addition to the discovery of frequent itemsets based on user defined minimum support. In order to achieve this, we first construct a tree containing the quantities of the items bought as well as the transactions which do not contain these items in a single scan of the database. Then by scanning the tree we can discover all frequent itemset based on user defined minimum quantity as well as support. This method is also found to be more efficient than Apriori and FP-tree, which require multiple scans of the database to discover all frequent itemsets based on user defined minimum support. |
| |
|
| |
|
| |
| 1 |
Jiawei Han Micheline Kamber, Data Mining Concepts and Techniques .Morgan Kaufman, San Francisco, CA, 2001
|
|
|
| 2 |
Han, J., Pei, J., Yin, Y. “Mining Frequent Patterns without Candidate Generation”, Proc. of ACM-SIGMOD International Conference Management of Data. Dallas, 2000, TX, 1-12.
|
|
|
| 3 |
Han J, Pei Jian, Runying Mao ” Mining Frequent Patterns without Candidate Generation: A Frequent Pattern Tree Approach” , Data Mining and Knowledge Discovery, Kluwer Academic Publishers, Netherland” 2004, pp 53-87.
|
|
|
| 4 |
R. Hemlata, A. Krishnan, C. Scenthamarai, R. Hemamalini. ”Frequent Pattern Discovery based on Co-occurrence Frequent Tree”. In Proceeding ICISIP-2005.
|
|
|
| 5 |
Ananthanarayana V. S, Subramanian, D.K., Narasimha Murthy M. “Scalable, distributed and dynamic mining of association rules” In Proceedings of HIPC'00. Springer Verlag Berlin,Heidelberg,2000, 559-566.
|
|
|
| 6 |
R. Srikant and R. Agarwal. “Mining generalized association rules”, Proceedings of International Conference on Very Large Data Bases 1995, pages 407-419.
|
|
|
| 7 |
Rakesh Agrawal, Tomasz Imielinski, Arun Swami, “Mining Association Rules between Sets of Items in Large databases”, Proceedings of the 1993 ACM SIGMOD Conference Washington DC, USA, May 1993
|
|
|
| 8 |
Rakesh Agarwal, Ramakrishnan Srikant, “Fast algorithms for mining Association Rules”, In proceedings of the 20th International Conference on Very Large databases, Santigo, 1994, pp 478-499.
|
|
|
| 9 |
S.Y.Wur and Y.Leu, “An effective Boolean Algorithm for mining Association Rules in large databases”, The 6th International conference on Database systems for Advanced Applications, 1999, pp 179-186.
|
|
|
| 10 |
IBM/Quest/Synthetic data.
|
|
|
| |
|
| |
|
| |
| 1 |
P. Kumar and V.S. Ananthanarayana, “Discovery of Weighted Association Rules Mining”, in Proceedings, Computer and Automation Engineering (ICCAE), The 2nd International Conference, Singapore, 26-28 Feb. 2010, pp. 718-722.
|
|
|
| 2 |
B. D. Satoto, D. O. Siahaan and A. Saikhu, “Perbaikan Struktur Weighted Tree Dengan Metode Partisi Fuzzy Dalam Pembangkitan Frequent Itemset”, Journal Ilmiah, KURSOR, Menuju Solusi Teknologi Informasi, 5(3), pp. 175-185, 2010.
|
|
|
| 3 |
N.V.V.Prasad, T.N. Lakshmi and D.Sujatha, “Disovery of Weighted Interesting Itemsets using Weighted Tree Approach”, International Journal of Computer Application, 1( 2), pp. 51-59, 2011.
|
|
|
| |
|
| |
|
| |
| 1 |
National Institute of Technology Karnataka
|
| 2 |
Manipal University
|
| 3 |
shendusou.com
|
| 4 |
KVG College of Engineering
|
| |
|
| |
|
| |
|
| Preetham Kumar : Colleagues
|
|
| Ananthanarayana V S : Colleagues
|
|
