Using the interestingness measure lift to generate association rules

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    In this digital age, organizations have to deal with huge amounts of data, sometimes called Big Data. In recent years, the volume of data has increased substantially. Consequently, finding efficient and automated techniques for discovering useful patterns and relationships in the data becomes very important. In data mining, patterns and relationships can be represented in the form of association rules. Current techniques for discovering association rules rely on measures such as support for finding frequent patterns and confidence for finding association rules. A shortcoming of confidence is that it does not capture the correlation that exists between the left-hand side (LHS) and the right-hand side (RHS) of an association rule. On the other hand, the interestingness measure lift captures such as correlation in the sense that it tells us whether the LHS influences the RHS positively or negatively. Therefore, using Lift instead of confidence as a criteria for discovering association rules can be more effective. It also gives the user more choices in determining the kind of association rules to be discovered. This in turn helps to narrow down the search space and consequently, improves performance. In this paper, we describe a new approach for discovering association rules that is based on Lift and not based on confidence.


  • Keywords


    Data Mining; Knowledge Discovery in Database (KDD); Association Rule Mining; Interestingness Measures.

  • References


      [1] R. Elmasri, S. B. Navathe, Fundamentals of database systems, sixth edition, Adeson-wesley publish, New York, 2011.

      [2] D. Hand, H. Mannila and P. Smyth, Principles of Data Mining, 2001, publish USA.

      [3] D. T. Larose, Discovering knowledge in data an introduction to data mining, first edition, Adeson-wesley publishes, USA, 2005.

      [4] Ayobami S. A., Rabi'u S., Knowledge Discovery in Database: A Knowledge Management Strategic Approach, Knowledge Management International Conference (KMICe) 2012, Johor Bahru, Malaysia, 4 – 6 July 2012.

      [5] Lou, Q., Advancing Knowledge Discovery and Data Mining. School of Electrical and Information engineering, WITN, China. IEEE Computer Society. 0-7695-3090-7/08, 2008.

      [6] M. D. Khatri, S. Dhande, History and Current and Future trends of Data mining Techniques, IJARCSMS International Journal of Advance Research in Computer Science and Management Studies, Vol.2, Issue 3, March 2014.

      [7] M. Ingle, N. Suravanshi, ”Review: Apriori Algorithms and Association Rule Generation and Mining”, AIJRSTEM American International Journal of Research in Science, Technology, Engineering & Mathematics, Published by IASIR International Association of Scientific Innovation and Research, USA, December 2013-Febryary 2014, pp. 180-183.

      [8] R. Agrawal, R. Srikant, Fast Algorithms for Mining Association Rules. In Proc. 1994 Int. Conf. Very Large Data Bases (VLDB’94), Santiago, Chile, pp. 487–499.

      [9] J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without candidate Generation. In Proc. 2000 ACM-SIGMOD Int. Conf. Managenment of Data (SIGMOD’00), PAGE 1 12, Dallas, TX, May 2000. http://dx.doi.org/10.1145/342009.335372.

      [10] C. Gyorödi, R. Gyorödi, and S. Holban "A Comparative Study of Association Rules Mining Algorithms", In: Proceeding SACI 2004, 1st Romanian-Hungarian Joint Symposium on Applied Computational Intelligence , Timisoara, Romania, May 25-26, 2004, pp. 213-222.

      [11] R. Agrawal, T. Imielinski, and A. Swami, Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering, 5(6): 914 925, December 1993.

      [12] C. S.Deora, S. Arora, and Z. Makani, Comparison of Interestingness Measures: Support-Confidence Framework versus Lift-Irule Framework, IJERA International Journal of Engineering Research and Applications, Vol. 3, Issue 2, March-April 2013.

      [13] Peter a. Flach and et al , Confirmation-Guided Discovery of First-Order Rules with Tertius, Machine Learning, 42, 61–95, 2001 °c 2001 Kluwer Academic Publishers. Manufactured in The Netherlands.

      [14] J. Arora, N. Bhalla, S. Rao, A Review on Association Rule Mining Algorithms, IJIRCCE International Journal of Innovative Research in Computer and Communication Engineering, Vol. 1, Issue 5, July 2013.

      [15] B. D. Dubey, M. Sharman, and R. Shah, “Comparative Study of Frequent Item Set in Data Mining”, IJPLA International Journal of Programming Languages and Applications, Indora, India,Vol. 5, No. 1, January 2015.

      [16] S. Pramod ., O.P. Vyas, Survey on Frequent Item set Mining Algorithms, IJCA International Journal of Computer Applications, Vol. 1, No. 15, 2010.

      [17] U. K. Pandey, S. Pal, A Data Mining view on Class Room Teaching Language, IJCSI International Journal of Computer Science, Vol.8, Issue 2, March 2011.

      [18] L. Geng, H. J. Hamilton, Interestingness Measures for Data Mining: A Survey, ACM Computing Surveys (CSUR), Vol.38, Issue 3, No. 9, 2006.


 

View

Download

Article ID: 4398
 
DOI: 10.14419/jacst.v4i1.4398




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.