Volume 5, Issue 1, July 2014, Pages 30–39
S. Jacinth Evangeline1, K.M. Subramanian2, and Dr. K. Venkatachalam3
1 Computer Science and Engineering, Erode Sengunthar Engineering College, Anna University Chennai, Tamilnadu, India
2 Computer Science and Engineering, Erode Sengunthar Engineering College, Anna University Chennai, Tamilnadu, India
3 Electrical Communication and Engineering, Vellalar College of Engineering and Technology, Anna University Chennai, Tamilnadu, India
Original language: English
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.
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.
S. Jacinth Evangeline1, K.M. Subramanian2, and Dr. K. Venkatachalam3
1 Computer Science and Engineering, Erode Sengunthar Engineering College, Anna University Chennai, Tamilnadu, India
2 Computer Science and Engineering, Erode Sengunthar Engineering College, Anna University Chennai, Tamilnadu, India
3 Electrical Communication and Engineering, Vellalar College of Engineering and Technology, Anna University Chennai, Tamilnadu, India
Original language: English
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.
