CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Survey On: Analysis of Pattern Mining and Behavior Prediction in M-Commerce

by Tejal V. Deshmukh, Anant M. Bagade
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 71 - Number 23
Year of Publication: 2013
Authors: Tejal V. Deshmukh, Anant M. Bagade
10.5120/12626-9338

Tejal V. Deshmukh, Anant M. Bagade . A Survey On: Analysis of Pattern Mining and Behavior Prediction in M-Commerce. International Journal of Computer Applications. 71, 23 ( June 2013), 19-23. DOI=10.5120/12626-9338

@article{ 10.5120/12626-9338,
author = { Tejal V. Deshmukh, Anant M. Bagade },
title = { A Survey On: Analysis of Pattern Mining and Behavior Prediction in M-Commerce },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 71 },
number = { 23 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume71/number23/12626-9338/ },
doi = { 10.5120/12626-9338 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:36:26.535772+05:30
%A Tejal V. Deshmukh
%A Anant M. Bagade
%T A Survey On: Analysis of Pattern Mining and Behavior Prediction in M-Commerce
%J International Journal of Computer Applications
%@ 0975-8887
%V 71
%N 23
%P 19-23
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Today is the world of science, mobile technologies, web applications, internet applications, business transactions these technologies can be combined to online business on mobile device which will increase the business performance. This is a real world practical application where a need arises to think about user using the system application which will run on mobile device. Thinking of user who makes actually transactions with the mobile device is a prime requirement. The main concept is to think about end user who interacts with the system taking into concern of m-commerce and mobile mobility, location area, data mining, behavior of user spending patterns which will have an enormous effect on business industry and to the society also. These methods also increase growth in various kinds of apps in mobile devices from low version phones to smart phones. So a need generates to use these in our transactions to locate shops, malls which will be situated at some distance far from our place and predict the user behavior in purchasing or making transactions into the shop with help of low version phones to smart phones. Since every user does not have smart phones so a system or an application should be made for the user using simple phones to generate automatic view of nearest shops or malls, with help of cellular phone. This will naturally make the business industry grow and give benefit to users which all counts for an m-commerce business economy industry.

References
  1. Eric Hsueh-Chan Lu, Wang- Chien Lee, Vincent S. Tseng "A Framework for Personal Mobile Commerce Pattern Mining and Prediction ",IEEE Transactions On Knowledge And Data Engineering, Vol. 24, No. 5, pp 769-782 MAY 2012.
  2. Glen Jeh, Jennifer Widom, "SimRank: A Measure of Structural-Context Similarity", Stanford University, pp 1-11.
  3. R. Agrawal and R. Srikant, "Fast Algorithm for Mining Association Rules," Proc. Int'l Conf. Very Large Databases, pp. 478-499, Sept. 1994.
  4. J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhu, "Mining Access Patterns Efficiently from Web Logs," Proc. Pacific Asia Conf. Knowledge Discovery and Data Mining, pp. 396-407, Apr. 2000.
  5. V. S. Tseng and W. C. Lin, "Mining Sequential Mobile Access Patterns Efficiently in Mobile Web Systems," Proc. Int'l Conf. Advanced Information Networking and Applications, pp. 867-871, Mar. 2005.
  6. C. H. Yun and M. S. Chen, "Mining Mobile Sequential Patterns in a Mobile Commerce Environment," IEEE Trans. Systems, Man, and Cybernetics, Part C, vol. 37, no. 2, pp. 278-295, Mar. 2007.
  7. Vincent S. Tseng , Kawuu W. Lin, "Efficient mining and prediction of user behavior patterns in mobile web systems" , Science Direct Institute of Computer Science and Information Engineering, National Cheng Kung University, No. 1, Ta-Hsueh Road, Tainan 701, 2006. [8 J. Pitkow, P. Pirolli, Mining longest repeating subsequences to predict World Wide Web surfing, in: Proceedings of the USENIX Symposium Internet Technologies and Systems, 1999.
  8. Jiawei Han, Jian Pei, Yiwen Yin, "Mining Frequent Patterns Without Candidate Generation", School of Computer Science, Simon Fraser University, pp 1-12.
  9. Jari Veijalainen, Vagan Terziyan, Henry Tirri," Transaction Management for M-Commerce at a Mobile Terminal", proceedings of the 36th Hawali International Conference on system Science-2003.
  10. Goswami D. N, Chaturvedi Anshu, Raghuvanshi C. S, "An Algorithm for Frequent Pattern Mining Based On Apriori" , International Journal on Computer Science and Engineering Vol. 02, No. 04, 942-947, 2010.
  11. ShopKick, http://www. shopkick. com/index. html, 2010.
Index Terms

Computer Science
Information Sciences

Keywords

M-commerce Mobile phones Mobile mobility K-nearest algorithm for capturing nearest areas(states cities) data mining behavior prediction images location area

Powered by PhDFocusTM