مقاله انگلیسی رایگان در مورد داده کاوی استفاده از وب اتوماتیک توسط روش KNN – الزویر 2016

 

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  PDF
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
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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.