CFP last date
15 October 2024
Reseach Article

Fast Mining of Finding Frequent Patterns in Transactional Database using Incremental Approach

by Pamli Basak, R.r. Sedamkar, Rashmi Thakur
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 9 - Number 2
Year of Publication: 2015
Authors: Pamli Basak, R.r. Sedamkar, Rashmi Thakur
10.5120/ijais15-451369

Pamli Basak, R.r. Sedamkar, Rashmi Thakur . Fast Mining of Finding Frequent Patterns in Transactional Database using Incremental Approach. International Journal of Applied Information Systems. 9, 2 ( June 2015), 6-10. DOI=10.5120/ijais15-451369

@article{ 10.5120/ijais15-451369,
author = { Pamli Basak, R.r. Sedamkar, Rashmi Thakur },
title = { Fast Mining of Finding Frequent Patterns in Transactional Database using Incremental Approach },
journal = { International Journal of Applied Information Systems },
issue_date = { June 2015 },
volume = { 9 },
number = { 2 },
month = { June },
year = { 2015 },
issn = { 2249-0868 },
pages = { 6-10 },
numpages = {9},
url = { https://www.ijais.org/archives/volume9/number2/754-1369/ },
doi = { 10.5120/ijais15-451369 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T18:59:44.672306+05:30
%A Pamli Basak
%A R.r. Sedamkar
%A Rashmi Thakur
%T Fast Mining of Finding Frequent Patterns in Transactional Database using Incremental Approach
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 9
%N 2
%P 6-10
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Datasets grow in size as they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial, software logs, microphones, wireless sensor networks and cameras. This paper presents a structure for simply, easily and competently parallelizing data mining algorithms for those huge datasets together with the incremental mining. MapReduce concept is use to execute the parallel FP-Growth algorithm by running the windows services parallel. The proposed algorithm eliminates duplicated work and spurious items. Also, it shortens the response time to a query for the set of frequent items. The proposed algorithm is implemented by parallel running of many windows services and experimental results shows tremendous advantages. The proposed algorithm runs 66% faster than the traditional algorithm of data mining. Also, memory utilization reduces by 37%.

References
  1. Han, Jiawei, Micheline Kamber, and Jian Pei. Data mining, southeast asia edition: Concepts and techniques. Morgan kaufmann, 2006.
  2. Hipp, Jochen, Ulrich Güntzer, and Gholamreza Nakhaeizadeh. "Algorithms for association rule mining—a general survey and comparison. " ACM sigkdd explorations newsletter 2, no. 1 (2000): 58-64.
  3. Cannataro, Mario, Domenico Talia, and Paolo Trunfio. "Distributed data mining on the grid. " Future Generation Computer Systems 18, no. 8 (2002): 1101-1112.
  4. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," in Int. Conf. VLDB, pages 487-499, September 1994.
  5. Jaiwei Han, Jian Pei and Yiwen Yin- "Mining Frequent Patterns without Candidate Generation," in Int. Conf. ACM-SIGMOID. pages 1-12, June 2000.
  6. H. Li, Y. Wang, D. Zhang, M. Zhang and E. Chang, PFP: Parallel FP-Growth for Query Recommendation, Proceedings of the 2008 ACM Conference on Recommender Systems, 2008, pages 107-114.
  7. A. Pradeepa, and A. S. Thanamani, PARALLELIZED COMPRISING FOR APRIORI ALGORITHM USING MAPREDUCE FRAMEWORK, International Journal of Advanced Research in Computer and Communication Engineering, vol. 2(11), 2013, pp. 4365-4368.
  8. Osmar R. Za¨, Ane Mohammad El-Hajj, Paul Lu, "Fast Parallel Association Rule mining Without Candidacy Generation", Natural Science and Engineering Research Council of Canada.
  9. D. W. Cheung, J. Han, V. T. Ng, and C. Y. Wong, "Maintenance of discovered association rules in large databases: an incremental updating technique. " in Int. Conf. on Data Engineering, pages 106-114, February 1996.
  10. T. F. Garib, M. Taha, and H. Nassar, "An efficient technique for incremental updating of association rules. " International Journal of hybrid Intelligent Systems, pages 45-53, May 2008.
  11. An, Hongmei, Ping Chen, and Lijing Huang. "Study of Incremental Updating Algorithm for Association Rules. " In Proceedings of the 2012 International Conference on Computer Application and System Modeling. Atlantis Press, 2012.
  12. Sun, Li, YuchenCai, Jiyun Li, and Juntao Lv. "An Efficient Algorithm for Updating Association Rules with Incremental Transactions and Minimum Support Changes Simultaneously. " In Proceedings of the 2012 Third Global Congress on Intelligent Systems, pp. 166-171. IEEE Computer Society, 2012.
  13. Tzung-Pei Hong, Chun-Wei Lin, Yu-Lung Wu, "Incrementally fast updated frequent pattern trees", in Elsevier Ltd: Expert Systems with Applications 34, pages 2424–2435, 2008.
  14. X. Wei, Y. Ma, F. Zhang, M. Liu, W. Shen, Incremental FP-Growth Mining Strategy for Dynamic Threshold value and Database Based on Mapreduce, Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design, May 2014, pages 271-276.
Index Terms

Computer Science
Information Sciences

Keywords

Incremental Data Mining Parallel FP-growth MapReduce jobs Incremental Parallel FP-growth.