出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Most website classification systems have dealt with the question of classifying websites based ontheir content, design, usability, layout and such, few have considered website classificationbased on users’ experience. The growth of online marketing and advertisement has lead tofierce competition that has resulted in some websites using disguise ways so as to attract users.This may result in cases where a user visits a website and does not get the promised results. Theresults are a waste of time, energy and sometimes even money for users. In this context, we designan experiment that uses fuzzy linguistic model and data mining techniques to capture users’experiences, we then use the k-means clustering algorithm to cluster websites based on a set offeature vectors from the users’ perspective. The content unity is defined as the distance betweenthe real content and its keywords. We demonstrate the use of bisecting k-means algorithm forthis task and demonstrate that the method can incrementally learn from user’s profile on theirexperience with these websites.
关键词：Website Classification; Fuzzy Linguistic Modeling; K-Means Clustering; Web Mining.
