IJCNS  Vol.8 No.12 , December 2015
Data Mining in Electronic Commerce: Benefits and Challenges
Abstract: Huge volume of structured and unstructured data which is called big data, nowadays, provides opportunities for companies especially those that use electronic commerce (e-commerce). The data is collected from customer’s internal processes, vendors, markets and business environment. This paper presents a data mining (DM) process for e-commerce including the three common algorithms: association, clustering and prediction. It also highlights some of the benefits of DM to e-commerce companies in terms of merchandise planning, sale forecasting, basket analysis, customer relationship management and market segmentation which can be achieved with the three data mining algorithms. The main aim of this paper is to review the application of data mining in e-commerce by focusing on structured and unstructured data collected thorough various resources and cloud computing services in order to justify the importance of data mining. Moreover, this study evaluates certain challenges of data mining like spider identification, data transformations and making data model comprehensible to business users. Other challenges which are supporting the slow changing dimensions of data, making the data transformation and model building accessible to business users are also evaluated. A clear guide to e-commerce companies sitting on huge volume of data to easily manipulate the data for business improvement which in return will place them highly competitive among their competitors is also provided in this paper.
Cite this paper: Ismail, M. , Ibrahim, M. , Sanusi, Z. and Nat, M. (2015) Data Mining in Electronic Commerce: Benefits and Challenges. International Journal of Communications, Network and System Sciences, 8, 501-509. doi: 10.4236/ijcns.2015.812045.

[1]   Cao, L., Li, Y. and Yu, H. (2011) Research of Data Mining in Electronic Commerce. IEEE Computer Society, Hebei.

[2]   Bhagyashree, A. and Borkar, V. (2012) Data Mining in Cloud Computing. Multi Conference (MPGINMC-2012).

[3]   Rao, T.K.R.K., Khan, S.A., Begun, Z. and Divakar, Ch. (2013) Mining the E-Commerce Cloud: A Survey on Emerging Relationship between Web Mining, E-Commerce and Cloud Computing. IEEE International Conference on Computational Intelligence and Computing Research, Enathi, 26-28 December 2013, 1-4.

[4]   Wu, M., Zhang, H. and Li, Y. (2013) Data Mining Pattern Valuation in Apparel Industry E-Commerce Cloud. IEEE 4th International Conference on Software Engineering and Service Science (ICSESS), 689-690.

[5]   Srinniva, A., Srinivas, M.K. and Harsh, A.V.R.K. (2013) A Study on Cloud Computing Data Mining. International Journal of Innovative Research in Computer and Communication Engineering, 1, 1232-1237.

[6]   Carbone, P.L. (2000) Expanding the Meaning and Application of Data Mining. International Conference on Systems, Man and Cybernetics, 3, 1872-1873.

[7]   Barry, M.J.A. and Linoff, G.S. (2004) On Data Mining Techniques for Marketing, Sales and Customer Relationship Management. Indianapolis Publishing Inc., Indiana.

[8]   Pan, Q. (2011) Research of Data Mining Technology in Electronic Commerce. IEEE Computer Society, Wuhan, 12-14 August 2011, 1-4.

[9]   Verma, N., Verma, A., Rishma and Madhuri (2012) Efficient and Enhanced Data Mining Approach for Recommender System. International Conference on Artificial Intelligence and Embedded Systems (ICAIES2012), Singapore, 15-16 July 2012.

[10]   Kamba, M. and Hang, J. (2006) Data Mining Concept and Techniques. Morgan Kaufmann Publishers, San Fransisco.

[11]   News Stack (2015).

[12]   Witten, I.H. and Frank, E. (2014) The Morgan Kaufmann Series on Data Mining Management Systems: Data Mining. 2nd Edition, Publisher Morgan Kaufmann, San Francisco, 365-528.

[13]   Liu, X.Y. And Wang, P.Z. (2008) Data Mining Technology and Its Application in Electronic Commerce. IEEE Computer Society, Dalian, 12-14 October 2008, 1-5.

[14]   Zeng, D.H. (2012) Advances in Computer Science and Engineering. Springer Heidelberg, NewYork.

[15]   Ralph, K. and Caserta, J. (2011) The Data Warehouse ETL Toolkit: Practical Techniques for Extraction, Cleaning, Conforming and Delivering Data. Wiley Publishing Inc., USA.

[16]   Michael, L.-W. (1997) Discovering the Hidden Secrets in Your Data—The Data Mining Approach to Information. Information Research, 3.

[17]   Li, H.J. and Yang, D.X. (2006) Study on Data Mining and Its Application in E-Business. Journal of Gansu Lianhe University (Natural Science), No. 2006, 30-33.

[18]   Raghavan, S.N.R. (2005) Data Mining in E-Commerce: A Survey. Sadhana, 30, 275-289.

[19]   Michael, J.A.B. and Gordon, S.L. (1997) Data Mining Techniques: For Marketing and Sales, and Customer Relationship Management. 3rd Edition, Wiley Publishing Inc., Canada.

[20]   Wang, J.-C., David, C.Y. and Chris, R. (2002) Data Mining Techniques for Customer Relationship Management. Technology in Society, 24, 483-502.

[21]   Christos, P., Prabhakar. R. and Jon, K. (1998) A Microeconomic View of Data Mining. Data Mining and Knowlege Discovery, 2, 311-324.

[22]   Yahoo (2001) Second Quarter Financial Report. Yahoo Inc., Califonia.