FP-GROWTH ALGORITHM BASED INCREMENTAL ASSOCIATION RULE MINING ALGORITHM FOR BIG DATA

Article Sidebar

PDF
Published: May 8, 2018
DOI: https://doi.org/10.26483/ijarcs.v9i2.5712
Keywords:
association rules, frequent patterns, apriori, FP-growth, incremental

Main Article Content

Ramya V
Research Scholar, Department of Computer Science, Bharathiyar University, Coimbatore, Tamil Nadu, India
Ramakrishnan M
Chairperson, School of Information Technology, Madurai Kamaraj University, Madurai, Tamil Nadu, India

Abstract

Discovering associations among huge collection of transactions is beneficial to rectify and to take appropriate decision made by decision makers. Discovering frequent itemsets is the key process in association rule mining. Since association rule mining process generates large number of rules which makes the algorithm inefficient is the biggest challenge for any and makes it difficult for the end users to comprehend the generated rules. The better idea is to use iterative technique to discover association rules. To overcome this problem, incremental updating of frequent itemsets is proposed in this paper. Proposed incremental data mining algorithm is based on FP-Growth and uses the concept of heap tree to address the issue of incremental updating of frequent itemsets. The proposed uses good tricks of FP-Growth, and significantly reduces the complexity. The experimental results show that the proposed algorithm reduces the execution time substantially and outperforms other algorithms.

Downloads

Download data is not yet available.

Article Details

Issue
Vol. 9 No. 2 (2018): March-April 2018
Section
Articles

COPYRIGHT

Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
  • The journal allows the author(s) to retain publishing rights without restrictions.
  • The journal allows the author(s) to hold the copyright without restrictions.

References

Groves, Peter, Basel Kayyali, David Knott, and Steve Van Kuiken. "The ‘big data’revolution in healthcare." McKinsey Quarterly 2 (2013): 3.

Wang, Xue Z. "Data Mining and Knowledge Discovery—an Overview." In Data Mining and Knowledge Discovery for Process Monitoring and Control, pp. 13-28. Springer, London, 1999.

Padhy, Neelamadhab, Dr.Mishra, and Rasmita Panigrahi. "The survey of data mining applications and feature scope." arXiv preprint arXiv:1211.5723 (2012).

Srivastava, Jaideep, Robert Cooley, Mukund Deshpande, and Pang-Ning Tan. "Web usage mining: Discovery and applications of usage patterns from web data." Acm Sigkdd Explorations Newsletter 1, no. 2 (2000): 12-23.

Manganaris, Stefanos, Marvin Christensen, Dan Zerkle, and Keith Hermiz. "A data mining analysis of RTID alarms." Computer Networks 34, no. 4 (2000): 571-577.

Fu, Xiaobin, Jay Budzik, and Kristian J. Hammond. "Mining navigation history for recommendation." In Proceedings of the 5th international conference on Intelligent user interfaces, pp. 106-112. ACM, 2000.

Katal, Avita, Mohammad Wazid, and R. H. Goudar. "Big data: issues, challenges, tools and good practices." In Contemporary Computing (IC3), 2013 Sixth International Conference on, pp. 404-409. IEEE, 2013.

Chen, Bin, Peter Haas, and Peter Scheuermann. "A new two-phase sampling based algorithm for discovering association rules." In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 462-468. ACM, 2002.

Mannila, Heikki, Hannu Toivonen, and A. Inkeri Verkamo. "E cient algorithms for discovering association rules." In KDD-94: AAAI workshop on Knowledge Discovery in Databases, pp. 181-192. 1994.

Chakaravarthy, Venkatesan T., Vinayaka Pandit, and Yogish Sabharwal. "Analysis of sampling techniques for association rule mining." In Proceedings of the 12th international conference on database theory, pp. 276-283. ACM, 2009.

Wontae Hwang and Dongseung Kim, â€Improved Association Rule mining by modified Trimmingâ€, Proc of the sixth IEEE international conf on Computer and Information Technology.

Cheung, Yin-Ling, and Ada Wai-Chee Fu. "Mining frequent itemsets without support threshold: with and without item constraints." IEEE Transactions on Knowledge and Data Engineering 16, no. 9 (2004): 1052-1069.

Hong, Tzung-Pei, Chun-Wei Lin, and Yu-Lung Wu. "Incrementally fast updated frequent pattern trees." Expert Systems with Applications 34, no. 4 (2008): 2424-2435.

Chiang, Fei, and Renée J. Miller. "Discovering data quality rules." Proceedings of the VLDB Endowment 1, no. 1 (2008): 1166-1177.

Sarath, K. N. V. D., and Vadlamani Ravi. "Association rule mining using binary particle swarm optimization." Engineering Applications of Artificial Intelligence 26, no. 8 (2013): 1832-1840.

Wu, Xindong, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan et al. "Top 10 algorithms in data mining." Knowledge and information systems 14, no. 1 (2008): 1-37.

Ghosh, Ashish, and Bhabesh Nath. "Multi-objective rule mining using genetic algorithms." Information Sciences 163, no. 1-3 (2004): 123-133.

Fung, Benjamin CM, Ke Wang, and Martin Ester. "Hierarchical document clustering using frequent itemsets." In Proceedings of the 2003 SIAM International Conference on Data Mining, pp. 59-70. Society for Industrial and Applied Mathematics, 2003.

Ahmed, Chowdhury Farhan, Syed Khairuzzaman Tanbeer, and Byeong-Soo Jeong. "An Efficient Method for Incremental Mining of Share-Frequent Patterns." In Web Conference (APWEB), 2010 12th International Asia-Pacific, pp. 147-153. IEEE, 2010.