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An Investigation into the Central Data Warehouse based Association Rule Mining

by Gurpreet Singh Bhamra, Anil Kumar Verma, Ram Bahadur Patel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 96 - Number 10
Year of Publication: 2014
Authors: Gurpreet Singh Bhamra, Anil Kumar Verma, Ram Bahadur Patel
10.5120/16827-6592

Gurpreet Singh Bhamra, Anil Kumar Verma, Ram Bahadur Patel . An Investigation into the Central Data Warehouse based Association Rule Mining. International Journal of Computer Applications. 96, 10 ( June 2014), 1-12. DOI=10.5120/16827-6592

@article{ 10.5120/16827-6592,
author = { Gurpreet Singh Bhamra, Anil Kumar Verma, Ram Bahadur Patel },
title = { An Investigation into the Central Data Warehouse based Association Rule Mining },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 96 },
number = { 10 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume96/number10/16827-6592/ },
doi = { 10.5120/16827-6592 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:21:21.268486+05:30
%A Gurpreet Singh Bhamra
%A Anil Kumar Verma
%A Ram Bahadur Patel
%T An Investigation into the Central Data Warehouse based Association Rule Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 96
%N 10
%P 1-12
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data Mining(DM) technique is used to mine interesting hidden knowledge from large databases using various computational techniques/ tools. Association Rule Mining(ARM) today is one of the most important aspects of DM tasks. In ARM all the strong association rules are generated from the Frequent Itemsets. In this study a central Data Warehouse based client-server model for ARM is designed, implemented and tested. The Outcome of this investigation and the advantages of software agents forms the base and motivation of using software agent technology in Distributed Data Mining.

References
  1. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedingsof the ACM-SIGMOD International Conference of Management of Data, pages 207–216, 1993.
  2. R. Agrawal and J. C. Shafer. Parallel mining of association rules. IEEE Transaction on Knowledge and Data Engineering, 8(6):962–969, 1996.
  3. Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules in Large Databases. In Proceedings of the 20th International Conference on Very Large Data Bases(VLDB'94), pages 487–499. Morgan Kaufmann Publishers Inc. , Sept. 12-15 1994.
  4. Kamal Ali Albashiri, Frans Coenen, and Paul Leng. EMADS: An extendible multi-agent data miner. Knowledge-Based Systems, 22(7):523–528, October 2009b.
  5. Gurpreet Singh Bhamra, Ram Bahadur Patel, and Anil Kumar Verma. An Encounter with Strong Association Rules. In Proceedings of IEEE International Advanced Computing Conference (IACC-2010), pages 342–346. Thapar University, Patiala, Punjab, India, IEEE, Feb 19-20 2010.
  6. Gurpreet Singh Bhamra, Ram Bahadur Patel, and Anil Kumar Verma. TDSGenerator: A Tool for generating synthetic Transactional Datasets for Association Rules Mining. International Journal of Computer Science Issues (IJCSI), 8(2):184–188, March 2011.
  7. Gurpreet Singh Bhamra, Ram Bahadur Patel, and Anil Kumar Verma. Intelligent Software Agent Technology: An Overview. International Journal of Computer Applications( IJCA), 89(2):19–31, March 2014.
  8. Frans Coenen, Graham Goulbourne, and Paul Leng. Tree Structures for Mining Association Rules. Data Mining and Knowledge Discovery, 8(1):25–51, January 2004.
  9. Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.
  10. J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2nd edition, 2006.
  11. Jiawei Han, Jian Pei, and Yiwen Yin. Mining frequent patterns without candidate generation. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pages 1–12. ACM, 2000.
  12. Jiawei Han, Jian Pei, Yiwen Yin, and Runying Mao. Mining Frequent Patterns without Candidate Generation: A Frequent- Pattern Tree Approach. Data Mining and Knowledge Discovery, 8(1):53–87, January 2004.
  13. Jochen Hipp, Ulrich Guntzer, and Gholamreza Nakhaeizadeh. Algorithms for association rule mining a general survey and comparison. ACM SIGKDD Explorations Newsletter, 2(1):58–64, June 2000.
  14. Renata Ivancsy, Ferenc Kovacs, and Istvan Vajk. An analysis of association rule mining algorithms. In Proceedings of the 4th International ICSC Symposium on Engineering of Intelligent Systems, pages 774–778, Feb. 29 - March 2 2004.
  15. Byung-Hoon Park and Hillol Kargupta. Distributed Data Mining: Algorithms, Systems, and Applications. Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle Baltimore, MD 21250, 2002.
  16. Grigorios Tsoumakas and Ioannis Vlahavas. Distributed Data Mining. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece, 2009.
  17. M. J. Zaki. Parallel and distributed association mining: a survey. IEEE Concurrency, 7(4):14–25, 1999.
Index Terms

Computer Science
Information Sciences

Keywords

Data Warehouse Frequent Itemsets Association Rule Mining

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