مشخصات مقاله | |
انتشار | مقاله سال 2016 |
تعداد صفحات مقاله انگلیسی | 19 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
منتشر شده در | نشریه الزویر |
نوع مقاله | ISI |
عنوان انگلیسی مقاله | Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method |
ترجمه عنوان مقاله | داده کاوی استفاده از وب اتوماتیک و سیستم نظریه پرداز توسط روش طبقه بندی KNN |
فرمت مقاله انگلیسی | |
رشته های مرتبط | مهندسی کامپیوتر |
گرایش های مرتبط | مهندسی نرم افزار |
مجله | محاسبات و اطلاعات کاربردی – Applied Computing and Informatics |
دانشگاه | Department of Computer Sc. & Technology – Ocean University of China – China |
کلمات کلیدی | خودکار؛ داده کاوی؛ نزدیک ترین همسایه K؛ آنلاین؛ زمان واقعی |
کلمات کلیدی انگلیسی | Automated; Data mining; K-Nearest Neighbor; On-line; Real-Time |
شناسه دیجیتال – doi |
http://dx.doi.org/10.1016/j.aci.2014.10.001 |
کد محصول | E8458 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
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1. Introduction
Data mining is the extraction of knowledge from large amount of observational data sets, to discover unsuspected relationship and pattern hidden in data, summa rize the data in novel ways to make it understandable and useful to the data users [13,31,2]. Web usage mining is the application of data mining technique to auto matically discover and extract useful information from a particular web site [2,22,30]. The term web mining was believed to have first came to be in 1996 by Etzioni in his paper titled ‘‘The World Wide Web: Quagmire or Gold mine’’ and since then attention of researchers world over has been shifted to this important research area [26]. In recent years, there has been an explosive growth in the number of researches in the area of web mining, specifically of web usage mining. According to Federico and Pier [9], over 400 papers have been published on web mining since the early paper published in 1990s. The Really Simple Syndication (RSS) reader website was developed for the purpose of reading dailies news on-line across the Globe, but lack ways of iden tifying client navigation pattern and cannot provide satisfactory Real-Time response to the client needs, so, finding the appropriate news becomes time con suming which makes the benefit of on-line services to become limited. The study aimed at designing and developing an automatic, online, Real-Time web usage data mining and recommendation system based on data mart technology. The system is able to observe users/clients navigation behavior by acting upon the user’s click stream data on the RSS reader web site, so as to recommend a unique set of objects that satisfies the need of an active user in a Real-Time, online basis. The user access and navigation pattern model are extracted from the historical access data recorded in the user’s RSS address URL file, using appropriate data mining techniques. |