CFP last date
22 April 2024
Reseach Article

Optimum Frequent Pattern Approach for Efficient Incremental Mining on Large Databases using Map Reduce

by Arpan H. Shah, Pratik A. Patel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 120 - Number 4
Year of Publication: 2015
Authors: Arpan H. Shah, Pratik A. Patel
10.5120/21217-3933

Arpan H. Shah, Pratik A. Patel . Optimum Frequent Pattern Approach for Efficient Incremental Mining on Large Databases using Map Reduce. International Journal of Computer Applications. 120, 4 ( June 2015), 25-29. DOI=10.5120/21217-3933

@article{ 10.5120/21217-3933,
author = { Arpan H. Shah, Pratik A. Patel },
title = { Optimum Frequent Pattern Approach for Efficient Incremental Mining on Large Databases using Map Reduce },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 120 },
number = { 4 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 25-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume120/number4/21217-3933/ },
doi = { 10.5120/21217-3933 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:05:22.953331+05:30
%A Arpan H. Shah
%A Pratik A. Patel
%T Optimum Frequent Pattern Approach for Efficient Incremental Mining on Large Databases using Map Reduce
%J International Journal of Computer Applications
%@ 0975-8887
%V 120
%N 4
%P 25-29
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining defines hidden pattern in data sets and association between the patterns. In data mining, association rule mining is key technique for discovering useful patterns from large collection of data. Frequent itemset mining is a famous step of association rule mining. Frequent itemset mining is used to gather item sets after discovering association rules. Some limitations exist with the traditional association rule mining algorithms for large-scale data. As for FP-Growth algorithm, the success is limited by internal memory size because mining process is on the base of large tree-form data structure. A new traditional approach, FP-growth technique is very efficient in large amount of data. FP-Growth algorithm constructs conditional frequent pattern tree and conditional pattern based from database which satisfies the minimum support. However, FP growth algorithm requires a tree storage structure, which results in high computation time. The proposed algorithm realizes to construct Optimum pattern Tree with the node as the data item of the transaction. This rare algorithm is implemented on Hadoop to reduce the computation cost. The Hadoop environment supports for handling the large data and process them in parallel manner for better performance. The optimal frequent pattern is obtained that satisfies the minimum support and confidence value.

References
  1. G. Xiaoting Wei, Yunlong Ma, Feng Zhang, Min Liu, Weiming 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, pp. 271-275
  2. Zahra Farzanyar, Nick Cercone "Efficient Mining of Frequent itemsets in Social Network Data based on MapReduce Framework" IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2013
  3. Arpan Shah, Pratik A. Patel, A Collaborative Approach of Frequent Item Set Mining: A Survey, IJCA, 2014, pp. 34-36
  4. Daniele Apiletti, Elena Baralis, Tania Cerquitelli, Silvia Chiusano, Luigi Grimaudo "SEARUM: a cloud-based Service for Association Rule Mining" 12th IEEE International Conference on Security and Privacy in Computing and Communications (Trust Com), 2013, pp. 1-8
  5. Le Wang, Lin Feng, Jing Zhang, Pengyu Liao, "An E?cient Algorithm of Frequent Itemsets Mining Based on Map Reduce", Journal of Information & Computational Science, May 20, 2014, pp. 2809–2816
  6. Zhuobo Rong ,DawenXia , Zili Zhang "Complex Statistical Analysis of Big Data: Implementation and Application of Apriori and FP­ Growth Algorithm Based on MapReduce", IEEE 2013,pp. 968 972
  7. Palak Patel, Prof. Purnima Gandhi. "Association Rule Mining Using Improved FP-Growth Algorithm" International Journal for Technological Research in Engineering Vol-1, Issue 10, June-2014 pp. 1173-1176
  8. M SUMAN, T ANURADHA, K GOWTHAM, A RAMAKRISHNA, "A Frequent Pattern Mining Algorithm Based on Fp-Tree Structure and Apriori algorithm", International Journal of Engineering Research and Applications (IJERA) Vol-2, Issue 1, Jan-Feb 2012, pp. 114-116 .
  9. Shravanth Oruganti, Qin Ding, Nasseh Tabrizi, "Exploring HADOOP as a Platform for Distributed Association Rule Mining" FUTURE COMPUTING The Fifth International Conference on Future Computational Technologies and Applications, 2013, pp. 63-67
  10. Anitha Modi, Radhika Krishnan, "An Improved Method for Frequent Itemset Mining", International Journal of Emerging Technology and Advanced Engineering ISSN 2250-2459, Volume 3, Issue 5, May 2013, pp. 143-146
  11. R. Prabamanieswari, "A Combined Approach for Mining Fuzzy Frequent Itemset" International Journal of Computer Applications (0975 – 8887) International Seminar on Computer Vision (ISCV-2013), pp. 1-5
  12. Jyoti Jadhav, Lata Ragha, Vijay Katkar "Incremental Frequent Pattern Mining" International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-1, Issue-6, August 2012, pp. 223-228
  13. SADHANA KODALI, KAMALAKAR M, CH. GAYATRI, K. PRAVALLIKA, "An Efficient Approach for the Implementation of FP tree", International Journal of Innovative Research in Computer and Communication Engineering, Vol. 1, Issue 7, September 2013, pp. 1446-1452
  14. Jyotsana Dixit, Abha Choubey, "A Survey of Various Association Rule Mining Approaches", International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 3, March 2014
  15. Pradeep Rupayla, Kamlesh Patidar, "A Comprehensive Survey of Frequent Item Set mining Methods", IJETAE, ISSN 2250-2459, Volume 4, Issue 4, April 2014, pp. 351-353
Index Terms

Computer Science
Information Sciences

Keywords

Association rules Data mining Frequent Item set Mining FP growth Large database Optimum pattern Tree