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IJESR/April 2014/ Vol-4/Issue-4/208-211 e-ISSN 2277-2685, p-ISSN 2320-9763

International Journal of Engineering & Science Research

ENHANCED MOBILE COMMERCE WITHPATTERN MINING AND PREDICTION

Shiju George1, Jency Mary Thomas*

2, Annu M. Kavukattu

2, Elizabeth Priya Mammen

2

1

Asst. Prof, Information Technology, Amal Jyothi College of Engineering, Kanjirapally, Kottayam, Kerala, India.2Information Technology, Amal Jyothi College of Engineering Kanjirapally, Kottayam, Kerala, India.

ABSTRACT

The mobile commerce has received a lot of interest from both the industry and the academia. In this paper, we propose a

novel framework, called Mobile Commerce Surveyor (MCS), for mining and prediction of mobile users movements and

purchase transactions. The MCS framework consists of three major components: 1) Similarity Model (SM) for measuring

the similarities among stores and items; 2) Personal Mobile Commerce Pattern Mine (PMCP-Mine) algorithm for

efficient discovery of mobile users Personal Mobile Commerce Pattern (PMCP); and 3) Personal Mobile Commerce

Behaviour Predictor (PMCBP) for prediction of the mobile user behaviors also recommends the mobile commerce users

with stores and items previously unknown to a user.

Keywords:Data mining, mobile commerce.

1. INTRODUCTION

With a wide range of mobile users and an immense increase in the advancement of wireless communication technology,

mobile users can access any data from anywhere and anytime [1] in the world and also do mobile transactions easily and

safely. In this paper, we look forward to explore the correlation between the moving behavior and purchasing

transactions of mobile users to explore potential M-Commerce features.

Because of the expeditious development of the web technology, many stores have made their store information (i.e.,

business hours, location and features available online like Google Map. Figure 1 show a scenario, where a user moves

among stores while making some purchases. Figure 1a shows a moving sequence, where underlined store labels indicate

some transactions being made there. Figure 1 shows the transactions done by the user, where item i 1 is purchased from

store A. The mobile transaction sequence generated by this user is {(A,{i1}), (B, ), (D, {i2}), (E,{i4}), (I, i3)}.

Fig 1: An example for a mobile transaction sequence showing a moving trajectory

The moving and purchase patterns of a user can be captured together as mobile commerce patternsfor mobile users. For

example a user does shopping as shown in Fig. 1 may show a moving pattern ABD and two patterns {(A,{i 1}) and (D,

{i2}). This pattern which can be expressed as {(A,{i1})(D,{i2})}, indicates that the user usually purchases item i1 in


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Copyright 2013 Published by IJESR. All rights reserved 209

store A and then purchases item i2 in store D on the specific path ABD. This brings a great effect in m-commerce by

giving a boost to the sales of store D when the user purchases item i1from store A.

To capture and obtain a better understanding of mobile users mobile commerce behaviours, data mining has been widely

used for discovering valuable information from complex data sets. They do not reflect the personal behaviors of

individual users to support M-Commerce services at a personalized level. Mobile Commerce or MCommerce is about the

explosion of applications and services that are becoming accessible from Internet-enabled mobile devices. It involves

new technologies, services and business models. It is quite different from traditional e-Commerce. Mobile phones havevery different constraints than the desktop computers.

2. PROBLEM DEFINITION

In the MCE framework, frequently moving locations and frequently purchased items are considered for analyzing mobile

users commerce behavior. The Personal Mobile Commerce Pattern-Mine (PMCP-Mine) algorithm was used to find only

frequent datasets, by deleting infrequent data in the Mobile Commerce Explorer Database. Also, recommendations were

done only for the frequent datasets. The similarity values that were found in the Similarity Inference Model (SIM) were

not accurate.

Our application is based on the mobile commerce pattern mining and behavior prediction for live shopping. In this paper,

we formulate the idea of implementing a framework called Mobile Commerce Explorer for the customers who need a

smart shopping by saving their time and energy as well. For that we need to have good solution for finding the item

similarity, method for behavior prediction and pattern mining.

3. LITERATURE SURVEY

Chan Lu, Lee and S. Tseng developed the Mobile Commerce Explorer Framework for mining and prediction of mobile

users movements and purchases [1].

Agrawal and Swami presented an efficient algorithm [2] that generates all significant association rules between items in

the database.

Han, Pei and Yin proposed a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree [3] structure

for storing compressed, crucial information about frequent patterns, and develop an efficient FPtree based mining

method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth.

V.S. Tseng and W.C. Lin found that which can discover patterns of sequential movement [4] associated with requested

services for mobile users in mobile web systems.

V.S. Tseng, H.C. Lu, and C.H. Huang proposed a novel data mining algorithm named TMSP-Mine for efficiently

discovering the Temporal Mobile Sequential Patterns[5] (TMSPs) of users in LBS environments.

4. EXISTING SYSTEM

As an existing system, a novel framework called MCE, for mining and prediction of mobile users movements and

transactions in mobile commerce environments is implemented. MCE framework has been implemented with three

components: Similarity Inference Model (SIM) for measuring the similarities among stores and items, Personal Mobile

Commerce Pattern Mine (PMCP-Mine) algorithm for efficient discovery of mobile users Personal Mobile Commerce

Patterns (PMCPs), Mobile Commerce Behavior Predictor (MCBP) for prediction of possible mobile user behaviors. In

the MCE framework, only frequently moved locations and frequently purchased items are considered

In our best knowledge, this is the first work that facilitates mining and prediction of personal mobile commerce behaviorsthat may recommend stores and items previously not known to a user. The experimental Evaluation of the framework has

been done. The framework achieves a very high precision in mobile commerce behavior predictions. The MCBP

prediction technique used in the framework integrates the mined PMCPs and the similarity information from SIM. The

experimental results show that the framework and three components are highly accurate and precise under various

conditions.


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The Existing system for our application area is that the customer who wants to purchase products needs to go to each

shop & search about the particular offers provided by each shops at the mall they are at. These facilities are available for

online shopping but not possible for the live shopping. The existing condition seems to be time consuming & tiresome.

5. PROPOSED SYSTEM

We have proposed an application for live shopping useful for the customers. With this application the customer acquiring

an Android phone can view the various offers provided by the different shops in a mall during their shopping. For that we

have planned to implement the framework for mobile commerce pattern mining & prediction for the mobile users

movements and transactions. In addition to that the proposed system will have a credit-point system which helps the

regular customers for getting the additional benefits based on their purchase history.

For a newly registered customer there wont be purchase history for predicting their next movement since there arent

any patterns to be mined. In such cases, we have planned to solve the situation by making use of an additional facility

that the customers can give their Interests during the registration. Thus the proposed system can do the predictions &

provide the details of offers based on their Interests.

We have also proposed to have a search option which will allow customers to search for a shop and not only get shops

that are similar or in their previous purchase pattern.

There are six modules in this proposed system:

5.1 Registration and approval module

This module is for the registration of Malls, Shops as well as the Customers. Here all the shops, malls and users who

wish to make use of the application can register.

5.2 Database management module

Database Management module is for all the database handling such as connectivity and updation/deletion/selection

management etc.

5.3 Product and category management module

This module is for Add/edit/delete the category hierarchy by the Admin and Add/edit/delete Products based on the

category by the shops. Hence each shop has the capability to update their item storage details as well as their new offers.

5.4 Similarity Inference module

This module focuses on the similarities between the shop and the item.

5.5 Pattern mining and prediction module

The users purchase history management, mined data analysis and User Identification are done in this module.

5.6 Billing Module

The billing module is for the item purchase management and to Add/edit/delete the bills. On the basis of billing of each

individual customer we decided to add one more facility that can provide a credit-point based system for getting the

additional benefits to the customers based on their purchasing. That is they can get additional benefits if they are the

regular customer having a very appreciable billing history.

6. CONCLUSION

In this paper, we proposed a new application for the customer which makes shopping more lively by saving their time

and money by making use of all the best offers provided at each shop. In addition to that here provides the functionality

for newly registered customers who do not have a previous purchasing history, to get the prediction based on their

Interests which they have entered at the time of customer registration.

Also we provide a credit system for the customers based on their billing history. The credit system helps the customers to

have some credit points based on their purchase and can make use of the additional benefits provided to them according

to their credit points. When a threshold value of credit points is reached, it can be used to generate coupons which can be


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used for acquiring discount during the time of purchase. Through these we can provide the facility for customers that

they can get additional benefited from each shop based on their purchasing history.

7. FUTURE WORKS

For the future work, we plan to explore more efficient mobile commerce pattern mining algorithm, design more efficient

similarity inference models, and develop profound prediction strategies to further enhance the MCE framework. In

addition, we plan to apply the MCE framework to other applications, such as object tracking sensor networks and

location based services, aiming to achieve high precision in predicting object behaviors. We also plan to bring in the idea

of augment reality into this application which can make shopping more interesting.

REFERENCES

[1] Lu EHC, Lee W-C, Tseng VS. A Framework for Personal Mobile Commerce Pattern Mining and Prediction. IEEE

transactions on knowledge and data engineering, 2012.

[2] Agrawal R, Imielinski T, Swami A. Mining Association Rule between Sets of Items in Large Database. Proc. ACM

SIGMOD Conf. Management of Data 1993; 207-216.

[3] Han J, Pei J, Yin Y. Mining Frequent Patterns without Candidate Generation. Proc. ACM SIGMOD Conf.

Management of Data 2000; 1-12.

[4] Tseng VS, Lin WC. Mining Sequential Mobile Access Patterns Efficiently in Mobile Web Systems. Proc. Intl Conf.Advanced Information Networking and Applications, Mar. 2005; 867-871.

[5] Tseng VS, Lu HC, Huang CH. Mining Temporal Mobile Sequential Patterns in Location-Based Service

Environments. Proc. Intl Conf. Parallel and Distributed Systems, Dec. 2007; 1-8.

[6] Ye Y, Zheng Y, Chen Y, Feng J, Xie X. Mining Individual Life Pattern Based on Location History. Proc. Intl Conf.

Mobile Data Management Systems, Services and Middleware, May 2009; 1-10.

[7] Pei J, Han J, Mortazavi-Asl B, Zhu H. Mining Access Patterns Efficiently from Web Logs. Proc. Pacific Asia Conf.

Knowledge Discovery and Data Mining, Apr. 2000; 396-407.

[8] ShopKick, http://www.shopkick.com/index.html, 2010.

[9] Agrawal R, Srikant R. Mining Sequential Patterns. Proc Intl Conf. Data Eng. Mar. 1995; 3-14.

[10] Yun CH, Chen MS. Mining Mobile Sequential Patterns in a Mobile Commerce Environment. IEEE Trans. Systems,Man, and Cybernetics, Part C, 2007; 37(2): 278-295.

[11] Tseng VS, Lin WC. Mining Sequential Mobile Access Patterns Efficiently in Mobile Web Systems. Proc. Intl Conf.

Advanced Information Networking and Applications, Mar. 2005; 867-871.

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