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International Journal of Innovation and Scientific Research
ISSN: 2351-8014
 
 
Tuesday 02 June 2020

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Efficiently Mining the Frequent Patterns in Mobile Commerce Environment


Volume 5, Issue 1, July 2014, Pages 30–39

 Efficiently Mining the Frequent Patterns in Mobile Commerce Environment

S. Jacinth Evangeline, K.M. Subramanian, and Dr. K. Venkatachalam

Original language: English

Received 5 May 2014

Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


Nowadays, a rapid development in the communication technology and increasing the usability of powerful portable devices, mobile users can use their mobile devices to access the information. One of the active areas is the mining and prediction of users' mobile commerce behaviors such as their movements and purchase transactions. The important issue is to mine the rare frequent items from database to satisfy the user needs. In this paper, we propose a technique that can efficiently satisfy the user needs. It predicts the frequent item based on the user selection. Systolic tree implementation is used to predict the frequently moved item in the database. The main aim is to recommend the stores and items previously to unknown user. We evaluate our system in real world and deliver good performance in terms efficiency and scalability.

Author Keywords: Data Mining, Frequent Pattern, Mobile Commerce, Prediction.


How to Cite this Article


S. Jacinth Evangeline, K.M. Subramanian, and Dr. K. Venkatachalam, “Efficiently Mining the Frequent Patterns in Mobile Commerce Environment,” International Journal of Innovation and Scientific Research, vol. 5, no. 1, pp. 30–39, July 2014.