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An Approach for Customer Behavior Analysis Using Web Mining

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An Approach for Customer Behavior Analysis Using Web Mining
An Approach for Customer Behavior Analysis
Using Web Mining

Preeti Sharma & Sanjay Kumar
NIT Raipur, Raipur, Chattishgarh, India
E-mail: preeti.nitu@gmail.com & Skumar.IT@nitrr.ac.in

Abstract - Customer satisfaction is the key secret of success for all industries regardless of whether it is web enabled or not. This paper focuses the role of web mining in achieving a viable edge in business. Web mining is becoming the tool for success for those who adopt electronic means of operation for conducting their business. Web mining is the application of data mining techniques to discover patterns from the Web through content mining, structure mining, and usage mining. Web mining can contribute to a large extent in gaining a competitive advantage in business. Business goals should be well understood.
Keywords - Web Minning, Log data, Data Mining, Sesssionzation, Frequent path.

I. INTRODUCTION Customer relationship is one of the major applications of Web mining. A website should be designed to entice the customers. Web Mining analyses visitor 's behavior and makes predictions on their future interaction. This can be exploited to improve website performance and to recommend products or links based on user 's behavior. Visitors entering the site exhibits different behavior. They might just surf through or the process might end up in a purchase. For understanding customer behavior and thus improve the performance of your web site, certain standards should be used like perform mining on web log data
A. WEB MINING: The information space known as Web is a collection of resources (Web resources) residing on the Internet, that can be accessed using HTTP and protocols that derive from it. A resource "can be anything that has identity. Familiar examples include an electronic document, an image, a service (e.g., "today 's weather report for Los Angeles"), as well as a collection of other resources. Not all resources are network "retrievable"; e.g., human



References: [1] Kim,Wooju. Song,Yong U. Hong ,June S. "Web enabled expert systems using hyperlink-based inference". Expert Systems with Applications. 2004. pp1-13. [2] Michele Facca, Federico [5] Arotaritei, Dragos. Mitra, Sushmita. "Web mining a survey in the fuzzy framework". Fuzzy Sets and Systems vol. 148, 2004. pp 5–19. [6] Larsen, Jan. Lars Hansen, Kai. Szymkowiak Have, Anna. Christiansen,Torben. Kolenda, Thomas. "Webmining learning from the World Wide Web". Computational Statistics & Data Analysis. 38. 2002. pp 517–532. [7] Eirinaki, Magdalini. Vazirgiannis, Michalis. "Web Mining for Web[19]. Gatetrade.net. Information on gatetrade.net and some of their solutions Marketplace Personalization". ACM Transactions on Internet Technology vol. 3, no.1, 2003. pp 1–27. [10] Dina Bitton, Bridging the Gap Between Database Theory and Practice", Cadre Technologies, Menlo Park, 1992. [11] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, Wadsworth, Belmont, 1984. [13] M. Kokar,Discovering Functional Formulas through Changing Representation Base", Proceedings of the Fifth National Conference on Artificial Intelligence, 1986, 455. [14] P. Langley, H. Simon, G. Bradshaw, and J. Zytkow, Scientific Discovery Computational Explorations of the Creative Process, The MIT Press, Cam- bridge, Mass., 1987. [15] Heikki Mannila and Kari-Jouku Raiha, Dependency Inference", VLDB-87, Brighton, England, 1987. [16] J. Ross Quinlan, Induction of Decision Trees", Machine Learning, 1, 1986.

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