دانلود رایگان مقاله انگلیسی داده کاوی استفاده از وب اتوماتیک و سیستم نظریه پرداز توسط روش طبقه بندی KNN - الزویر 2016

Abstract

The major problem of many on-line web sites is the presentation of many choices to the client at a time; this usually results to strenuous and time consuming task in finding the right product or information on the site. In this work, we present a study of automatic web usage data mining and recommendation system based on current user behavior through his/her click stream data on the newly developed Really Simple Syndication (RSS) reader website, in order to provide relevant information to the individual without explicitly asking for it. The K-Nearest-Neighbor (KNN) classification method has been trained to be used on-line and in Real-Time to identify clients/visitors click stream data, matching it to a particular user group and recommend a tailored browsing option that meet the need of the specific user at a particular time. To achieve this, web users RSS address file was extracted, cleansed, formatted and grouped into meaningful session and data mart was developed. Our result shows that the K-Nearest Neighbor classifier is transparent, consistent, straightforward, simple to understand, high tendency to possess desirable qualities and easy to imple ment than most other machine learning techniques specifically when there is little or no prior knowledge about data distribution.

6. Conclusion

Our work provides a basis for automatic Real-Time recommendation system. The system performs classification of users on the simulated active sessions extracted from testing sessions by collecting active users’ click stream and matches this with similar class in the data mart, so as to generate a set of recommendations to the client in a Real-Time basis.

The result of our experiment shows that an automatic Real-Time recommenda tion engine powered by K-NN classification model implemented with Euclidean distance method is capable of producing useful and a quite good and accurate classifications and recommendations to the client at any time based on his immediate requirement rather than information based on his previous visit to the site.