Applying Improved Clustering Algorithm into EC Environment Data Mining

Abstract:

With the rising growth of electronic commerce (EC) customers, EC service providers are keen to analyze the on-line browsing behavior of the customers in their web site and learn their specific features. Clustering is a popular non-directed learning data mining technique for partitioning a dataset into a set of clusters. Although there are many clustering algorithms, none is superior for the task of customer segmentation. This suggests that a proper clustering algorithm should be generated for EC environment. In this paper we are concerned with the situation and proposed an improved k-means algorithm, which is effective to exclude the noisy data and improve the clustering accuracy. The experimental results performed on real EC environment are provided to demonstrate the effectiveness and feasibility of the proposed approach.

• Periodical: Applied Mechanics and Materials (Volume 596)
• Pages: 951-959
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.596.951
• Online since: July 2014
• Authors: Yu Peng Ma*, Bo Ma, Tong Hai Jiang
• Keywords: Customer Segmentation, EC Environment, Improved K-Means, K-Mean

References

[1] Barak A., Gelbard R., Classification by clustering decision tree-like classifier based on adjusted clusters,; Expert Systems with Applications, 38, 7, 2011, 8220-8228.

DOI: 10.1016/j.eswa.2011.01.001

[2] Srivatsava, J., R. Cooley, M. Deshpande and P.N. Tan, 2000. Web usage mining: discovery and applications of usage patterns from Web data. ACM SIGKDD Explorat. Newsletter, 1: 12-23. DOI: 10. 1145/846183. 846188.

DOI: 10.1145/846183.846188

[3] Jianxi Zhang, Peiying Zhao, Lin Shang and Lunsheng Wang, Web Usage Mining Based On Fuzzy Clustering in Identifying Target Group, International Colloquium on Computing, Communication, Control, and Management, Vol. 4, Pp. 209-212, (2009).

DOI: 10.1109/cccm.2009.5267789

[4] R. Dubes, Cluster analysis and related issues, in: C. Chen, L. Pau, P. Wang (Eds. ), Handbook of Pattern Recognition and Computer Vision, World Scientific Publishing Co. Inc., River Edge, NJ, 1993, p.3–32.

DOI: 10.1142/9789814343138_0001

[5] D. Fisher, Knowledge acquisition via incremental conceptual clustering, Machine Learning 2, 1987, 139–172.

DOI: 10.1007/bf00114265

[6] L. Wallace, M. Keil, A. Rai, Understanding software project risk: a cluster analysis, Information and Management 42, 2004, 115–155.

DOI: 10.1016/j.im.2003.12.007

[7] Innovations in Intelligent Machines, p.1–19. Springer, Heidelberg, (2011).

[8] Patnaik, Sovan Kumar, Soumya Sahoo, and Dillip Kumar Swain, Clustering of Categorical Data by Assigning Rank through Statistical Approach, International Journal of Computer Applications 43. 2: 1-3, (2012).

DOI: 10.5120/6072-7456

[9] Manish Verma, Mauly Srivastava, Neha Chack, Atul Kumar Diswar, Nidhi Gupta, A Comparative Study of Various Clustering Algorithms in Data Mining, International Journal of Engineering Reserch and Applications (IJERA), Vol. 2, Issue 3, pp.1379-1384, (2012).

[10] Anoop Kumar Jain, Satyam Maheswari, Survey of Recent Clustering Techniques in Data Mining, International Journal of Computer Science and Management Research, Volume 1, issue 1, 2013, 72-78.