Recommendation system with Automated Web Usage data mining using K-Nearest Neighbor(KNN) classification.

The major problem of many on-line web sites is the presentation of many choices to the various clients at a time. This usually results into time consumingtask in finding outthe right product or information on the site. The user's current interest depends upon the navigational behavior which helps the organizations to guide users in their browsing activities and obtain some relevant information in a short span of time. Since, the resulting patterns which are obtained through data mining techniques did not perform well in the prediction of future browsing patterns because of the low matching rate of resulting rules and of user's browsing behavior. This paper focuses on the study of the automatic web usage data mining and recommendation system which is based on current user behavior through his/her click stream data. The K-Nearest-Neighbor (KNN) classification method has been trained to be used in real-time and on-line to identify clients and visitors click stream data, matching it to a particular user group and recommends a tailored browsing option that meet the needs of the specific user at particular time. [ABSTRACT FROM AUTHOR]