EST

Call for paper
April Edition 2017

International Journal of Applied Information Systems solicits high quality original research papers for the
March 15, 2017
April 2017 Edition of the journal.
The last date of research paper submission is
March 15, 2017
SUBMIT YOUR PAPER

Number 10

Data Mining in Market Basket Transaction: An Association Rule Mining Approach

User Rating: 0 / 5

Star InactiveStar InactiveStar InactiveStar InactiveStar Inactive
 

PrintEmail

journal image
 Download
1288
  • S.o. Abdulsalam and K.s. Adewole and A.g. Akintola and M.a. Hambali 2014. Data Mining in Market Basket Transaction: An Association Rule Mining Approach. International Journal of Applied Information Systems. 7, 10 (October 2014), 15-20. DOI=http://dx.doi.org/10.5120/ijais451244
  • @article{10.5120/ijais2017451568,
    author = {S.o. Abdulsalam and K.s. Adewole and A.g. Akintola and M.a. Hambali},
    title = {Data Mining in Market Basket Transaction: An Association Rule Mining Approach},
    journal = {International Journal of Applied Information Systems},
    issue_date = {October 2014},
    volume = {7},
    number = {},
    month = {October},
    year = {2014},
    issn = {},
    pages = {15-20},
    numpages = {},
    url = {/archives/volume7/number10/686-1244},
    doi = { 10.5120/ijais14-451244},
    publisher = { xA9 2013 by IJAIS Journal},
    address = {}
    }
    
  • %1 451244
    %A S. O.  Abdulsalam
    %A K. S.  Adewole
    %A A. G.  Akintola
    %A M. A.  Hambali
    %T Data Mining in Market Basket Transaction: An Association Rule Mining Approach
    %J International Journal of Applied Information Systems
    %@ 
    %V 7
    %N 
    %P 15-20
    %D 2014
    %I  xA9 2013 by IJAIS Journal
    

Abstract

Failure of wireless link is considered as one of popular challenges faced by Mobile Ad-Hoc Networks (MANETs). Whereas this type of networks does not have any fixed routers or any pre-exist infrastructure. Also, every node is capable of movement and can be connected to other nodes dynamically. Therefore, the network topology will be changed frequently and unpredictably according to continuous interaction between nodes that simultaneously affect network topology in the basis of dynamic ad-hoc nature. This factor puts routing operation in critical area of research under mobile ad-hoc network field due to highly dynamic environment. To adapt this nature, MANETs demand new routing strategies to occupy these challenges. Thereafter, huge amount of protocols are proposed to argue with ad-hoc requirements. Thus, it is quite difficult to specify which protocols perform better under different mobile ad-hoc scenarios. This paper examines the prominent routing protocols that are designed for mobile ad-hoc networks by describing their structures, operations, features and then comparing their various characteristics.

References

  1. Mulhern, F. N. and Leone, R. P. 1991. Implicit Price Bundling of Retail Products: A Multiproduct Approach to Maximizing Store Probability, Journal of Marketing, 55, pp. 63-76.
  2. Chib S. , Nardari F. and Shephard N. 2002. Markov Chain Monte Carlo Methods for Stochastic Volatility Models, Journal of Econometrics, 108, pp. 281-316.
  3. Russell, G. J. and Petersen A. 2000. Analysis of Cross Category Dependence in Market Basket Selection, Journal of Retailing.
  4. Han, J. and Kamber, M. 2006. Data Mining: Concepts and Techniques, 2nd Edition, Morgan Kaufmann Publishers.
  5. Fayyad, U. M. , Piatetsky-Shapiro, G. , and Smyth, P. 1996. From data mining to knowledge discovery in databases. AI Magazine, 17(3), pp. 37-54.
  6. Oladipupo, O. O. and Oyelade, O. J. 2010. Knowledge Discovery from Students' Result Repository: Association Rule Mining Approach, International Journal of Computer Science and Security, 4(2), pp. 199-207.
  7. Agarwal, P. Yadav, M. L. and Anand, N. 2013. Study on Apriori Algorithm and its Application in Grocery Store, International Journal of Computer Applications, 74(14), pp. 1-8.
  8. Tissera, W. M. R. , Athauda, R. I and Fernando, H. C. 2006. Discovery of Strongly Related Subjects in the Undergraduate Syllabi using Data Mining, IEEE International Conference on Information Acquisition, pp. 57-62.
  9. Phani, Prasad J. and Murlidher, Mourya 2013. A Study on Market Basket Analysis using a Data Mining Algorithm, International Journal of Emerging Technology and Advanced Engineering, 3(6), pp. 361-363.
  10. Dhanabhakyam, M. and Punithavalli, M. 2011. A Survey on Data Mining Algorithm for Market Basket Analysis, Global Journal of Computer Science and Technology, 11(11), pp. 23-28.
  11. Bartik, V. 2009. Association based Classification for Relational Data and its Use in Web Mining, CIDM '09, IEEE Symposium on Computational Intelligence and Data Mining, pp. 252-258.
  12. Sumithra, R. and Paul, S. 2010. Using Distributed Apriori Association Rule and Classical Apriori Mining Algorithms for Grid Based Knowledge Discovery, International Conference on Computing Communication and Networking Technologies, pp. 1-5.
  13. Qiang Niu, Shi-Xiong Xia, and Lei, Zhang 2009. Association Classification Based on Compactness of Rules, WKDD 2009, Second International Workshop on Knowledge Discovery and Data Mining, pp. 245-247.
  14. Lin, L. and Pei-qi, L. 2001. Study on an Improved Apriori Algorithm and its Application in Supermarket.
  15. Tan, P. N. , Steinbach M. and Kumar V. 2006. Introduction to Data Mining, Addison Wesley.
  16. Agrawal, R. , Imielinski, T. and Swami, A. 1993. Mining Association Rules between Sets of Items in Large Databases, Proceedings of the ACM SIGMOD International Conference on Management of Data.
  17. Association Rule Learning 2011. Retrieved January 12, 2014 from http://en. wikipedia. org/wiki/Association_ rule_learning
  18. Larissa, T. M. 2003. Introduction to Data Mining, London: Oxford University Press.

Keywords

Data Mining, Association Rule, Market Basket Analysis, Apriori Algorithm

Index Terms

Computer Science
Information Sciences