期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：6
出版社：S.S. Mishra
摘要：In the internet era websites on the internet are helpful source of information for nearly every activity. Thus there's a fast development of World Wide Web in its volume of traffic and the size and complexional of websites. There are varieties of issues connected with the existing web usage mining approaches. Existing web usage mining algorithms suffer from problem of practical applicability. So, a completely unique analysis is extremely abundant necessary for the accurate prediction of future performance of web users with fast execution time. This paper consists of preprocessing and clustering of web users. Log data is routinely noisy and unclear, so preprocessing is an essential process for effective mining process. We present a novel approach to novel pre-processing of removing local and global noise and web robots and clustering Web site users into different groups and generating common user profiles. These profiles can be used to make recommendation, personalize Web sites, and for other uses such as targeting users for advertising. This FPCM (Fuzzy Possibilistic C Means) algorithm is relatively simple to use and gives comparable results with FCM (Fuzzy C Means) reported in the literature of web mining. Anonymous Microsoft Web Dataset is used for evaluating the proposed preprocessing technique and clustering process
