دانلود مقاله ISI انگلیسی شماره 5577
عنوان فارسی مقاله

طراحی و پیاده سازی سیستم هوشمند وب کاوی در زمینه تجارت الکترونیک

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
5577 2012 8 صفحه PDF سفارش دهید 2430 کلمه
خرید مقاله
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عنوان انگلیسی
Design and Implementation of Web Usage Mining Intelligent System in the Field of e-commerce
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Procedia Engineering, Volume 30, 2012, Pages 20–27

کلمات کلیدی
- داده کاوی - بازیابی اطلاعات - گسترش خدمات وب سایت - استفاده از وب کاوی - محاسبات وب سایت -
پیش نمایش مقاله
پیش نمایش مقاله طراحی و پیاده سازی سیستم هوشمند وب کاوی در زمینه تجارت الکترونیک

چکیده انگلیسی

The rising popularity of electronic commerce makes data mining an indispensable technology for several applications, especially online business competitiveness. The World Wide Web provides abundant raw data in the form of web access logs. Now a days many business applications utilizing data mining techniques to extract useful business information on the web evolved from web searching to web mining. This paper introduces a web usage mining intelligent system to provide taxonomy on user information based on transactional data by applying data mining algorithm, and also offers a public service which enables direct access of website functionalities to the third party.

مقدمه انگلیسی

The goal of Web Usage Mining is to find out extract the useful information from web data or web log files. The other goals are to enhance the usability of the web information and to apply the technology on the web applications, for instance, pre - fetching and catching, persona lization etc. For decision management, the result of web usage mining can be used for target advertisement, improving web design, improving satisfaction of customer, guiding the strategy decision of the enterprise and market analysis [1]. Recently there a re a large number of web services that we can use and many of them are open source based . Web services are APIs that facilitate the communication between applications for example RapidMiner, Digg.com, Amazon, eBay are opened access to their services and da ta through APIs, and we an make use of their services for the development of web usage mining research applications. The concept of Web APIs enables direct access to the website functionalities in order to leverage third party efforts on value adding serv ices [ 2 ]. However, the number of companies, services or web sites that gather information about users increasing continuously. These systems store private information about users and for that reason appears much controversy about the legitimacy. The main p roblem is that th e s e companies do n’t share information with the rest of the world. In this paper, we present a public system to store information about their products and view details about user behavior. Some of the problems about sharing information woul d be solved if there was a public service for user behavior information. If all people can access that information, all of them will have the same opportunities and will be at the same point in a commercial environment [2] . Th e rest of the paper is organi zed as various sections: section 2 will have implemented details about how Hierarchical Agglomerative Clustering applied on sample web log for mobile marketing. Section 3 elaborates how to provide public service (API) which enables third party to view thei r customer’s behavior. Finally Section 4 demonstrates experimental result and Section 5 Conclusion with future work.

نتیجه گیری انگلیسی

The importance of web usage mining is unquestionable with the rising importance of the web not only as an information portal but also as a business edge. Web access logs contain abundant raw data that can be mined for web access patterns, which in turn can be applied to improve the overall surfing experience of users. By taking into con sideration we have mainly focused on designing of web usage mining intelligent system for clustering of user behaviors using agglomerative clustering algorithm. Experiments conducted on web logs show the viability of our approach. However, much work is sti ll needed to add more functionality to web mining services, to make web usage mining more useful in the electronic commerce domain.

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