Enhanced Association Rule Mining Algorithm (EARMA) for Reducing Computational Time on Large Data Set

Authors

  • priyanka rana Department of Information Technology, Chandigarh Engineering College, Mohali, Punjab, India
  • Jaspreet Singh Department of Information Technology, Chandigarh Engineering College, Mohali, Punjab, India
  • Dr. Shashi Bhushan Department of Information Technology, Chandigarh Engineering College, Mohali, Punjab, India

DOI:

https://doi.org/10.37591/rrdms.v3i2.797

Keywords:

Data mining, utility mining, high utility item set mining, frequent item set

Abstract

Association rule learning is a trendy process for discovering exciting relationships between variables in big database. It is frequently used in market basket analysis field e.g. if a buyer buys onions and potatoes then he also purchases beef. But, in fact, it can be implemented in different application area where we want to determine the association among variables. The APRIORI method is definitely the trendiest. But, even with its good quality property, this procedure has a drawback: the number of obtained rules can be very high. The capabilities to highlight the most exciting rules, those which are related, become a major challenge.

Cite this Article
Priyanka Rana, Jaspreet Singh, Shashi Bhushan. Enhanced Association Rule Mining Algorithm (EARMA) for Reducing Computational Time on Large Data Set. Research & Reviews: Discrete Mathematical Structures. 2016; 3(2): 20–25p.

References

AL-Zawaidah Farah Hanna, Yosef Hasan Jbara, Marwan AL. An Improved Algorithm for Mining Association Rules in Large Databases. World of Computer Science and Information Technology. 2011; 1(7): 311–316p.

Liu Yanxi. Study on Application of Apriori Algorithm in Data Mining. Computer Modeling and Simulation, 2010, ICCMS'10. Second International Conference on. IEEE. 2010; 3.

Yadav Chanchal, Shuliang Wang, Manoj Kumar. Amity University Noida, UP, India. Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on. IEEE; 2013.

Al-Maolegi Mohammed, Bassam Arkok. An Improved Apriori Algorithm for Association Rules. International Journal on Natural Language Computing (IJNLC). 2014; 3(1).

Yang Jun, et al. An Improved Apriori Algorithm Based on Features. Computational Intelligence and Security (CIS). 2013.

Shankar S, Purusothaman T, Jayanthi S. Novel Algorithm for Mining High Utility Item sets. IEEE, Computing, Communication and Networking, 2008. ICCCN 2008. International Conference on. 2008.

Guo-Cheng Lan, Tseng VS, Tzung-Pei Hong, et al. Using Pruning and Filtering Strategies to Speed-Up Projection-Based Utility Mining. IEEE, System Science and Engineering (ICSSE), 2011 International Conference on. 2011.

Zhou Lin, Ying Liu, Jing Wang, et al. Utility-Based Web Path Traversal Pattern Mining. IEEE, Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on. 2007. 9. Fournier-Viger Philippe, Cheng-Wei Wu, Souleymane Zida, et al. Fhm: Faster High-Utility Itemset Mining Using Estimated Utility Co-Occurrence Pruning. In Foundations of Intelligent Systems. 2014; 83–92p.

Yu Guangzhu, Shihuang Shao, Xianhui Zeng. Mining Long High Utility Item Sets in Transaction Databases. WSEAS Transactions on Information Science & Applications. 2008; 5(2): 202–210p.

Junqiang Liu, Ke Wang, Fung BCM. Direct Discovery of High Utility Itemsets without Candidate Generation. IEEE, Data Mining (ICDM), 2012 IEEE 12th International Conference on. 2012.

Shankar S, Purusothaman T, Jayanthi S, et al. A Fast Algorithm for Mining High Utility Itemsets. IEEE, Advance Computing Conference, 2009. IACC 2009. IEEE International. 2009.

Tzung-Pei Hong, Cho-Han Lee, Shyue-Liang Wang. An Incremental Mining Algorithm for High Average-Utility Itemsets. IEEE Pervasive Systems, Algorithms, and Networks (ISPAN), 2009 10th International Symposium on. 2009.

Downloads

Published

2016-09-06

Issue

Vol. 3 No. 2 (2016)

Section

Research Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.