Enhancement of Online Web Recommendation System using a Hybrid Clustering and Pattern Matching Approach
Dipali Wankhede1, S. G. Tuppad2

1Dipali Wankhede, Department of Computer Science & Engineering, BAMU Matsyodari Shikshan Sansthas College of Engineering and Technology ,Jalna, Aurangabad (Maharashtra), India.
2Prof. S.G. Tuppad, Assistant Professor, Matsyodari Shikshan Sanstha’s College of Engineering and Technology, Jalna, Aurangabad (Maharashtra), India.

Manuscript received on 15 February 2017 | Revised Manuscript received on 22 February 2017 | Manuscript Published on 28 February 2017 | PP: 189-192 | Volume-6 Issue-3, February 2017 | Retrieval Number: C4859026317/17©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Increasing the amount of information over the Internet in recent years has led to the increased risk of flooding of information which in turn has created the problem of access to relevant data users. Also with the rise in the number of websites and web pages, webmasters find it difficult to make the content according to user need. Demand for information Users can imagine evaluating web user browsing behavior. Web Usage Mining (WUM) is used to extract knowledge from access logs Web user by using Data mining techniques. One of the applications is WUM recommendation system that is customized information filtering technique used to determine whether any of a user approved a particular article or to identify a list of items that it can be of great importance to the user. In this document architecture that integrates product information with the user access to log data and then generates a set of recommendations for it is presented that particular user. The application has registered encouraging in terms of precision, recall and F1 results metrics.
Keywords: Web Usage Mining, Online Web Recommendation System, Clustering, Pattern Matching, Boyer Moore, K-Means, Recommendation.

Scope of the Article: Clustering