Recommendation system is used to generate the recommendations on the basis of the input and processed data. This study develops a web usage data mining based recommendation system. To perform the classification and pattern matching is the major task of a recommendation system. For the purpose of classification, traditional recommendation system prefers the KNN classifiers but the issue was that the KNN performs the classification and pattern matching on the basis of the nearest neighbour and thus in this manner, it lacks the scalability and fails to perform the exact matches for the recommendation system. Therefore the Naïve Bayes classifier is implemented and analyzed in this study for the recommendation system and after simulation, it is found that the Naïve Bayes classifier generates the highly accurate and error-free recommendations for the users. The JAVA platform is used for simulation and the results are evaluated in the form of Accuracy, Error Rate, RMSE and Precision.

Data Mining, Web Usage Data mining, Classification, Pattern Matching, Naïve Bayes Classifiers

[1] D.A. Adeniyi, Z. Wei, Y. Yongquan, “Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method”, ELSEVIER, Vol. 12, pp. 90-108, 2014.
[2] Ngoc Nhu Van, J. Rokne, “Integrating SOM and Fuzzy K-means Clustering for Customer Classification in Personalized Recommendation System for Non-Text based Transactional Data”, International Conference on Information Technology, Amman, Jordan, 2017.
[3] Anitha Talakokkula, “A Survey on Web Usage Mining, Applications and Tools”, Computer Engineering and Intelligent System, Vol. 6, No.2, pp. 22-30, 2015.
[4] Bo Cheng, Shuai Zhao, Changbao Li, Junliang Chen, “A Web Services Discovery Approach Based on Mining Underlying Interface Semantics”, IEEE, Vol. 29, pp 950-962, 2017.
[5] Satya Prakash Singh , Meenu, “Analysis of web site using web log expert tool based on web data mining”, IEEE, 2017.
[6] Yeqing Li, “Research on Technology, Algorithm and Application of Web Mining”, IEEE, Vol. 1, pp. 772-775, 2017.
[7] Z. A. Usmani, Saiqa Khan, Mustafa Kazi, Aadil Bhatkar, Shuaib Shaikh, “ZAIMUS: A department automation system using data mining and web technology”, IEEE, pp 1-6, 2017.
[8] Martin Lnenicka , Jan Hovad , Jitka Komarkova , Miroslav Pasler, “A proposal of web data mining application for mapping crime areas in the Czech Republic”, IEEE, 2016.
[9] Viktor Medvedev, Olga Kurasova, Gintautas Dzemyda, “A new web-based solution for modelling data mining processes”, ELSEVIER, Vol. 76, pp. 34-46, 2016.
[10] Petar Ristoski, Heiko Paulheim, “Semantic Web in data mining and knowledge discovery: A comprehensive survey”, ELSEVIER, Vol. 36, pp. 1-22,2016.
[11] Venkata Subba Reddy Poli, “Fuzzy data mining and web intelligence”, IEEE, 2016.
[12] Zoltán Balogh, “Data-mining behavioural data from the web”, IEEE, Vol.1, pp. 122-127, 2016.
[13] Suvarn Sharma, Amit Bhagat, “Data preprocessing algorithm for Web Structure Mining”, IEEE, pp. 94-98, 2016.
[14] Wang Lei , Liu Chong, “Implementation and Application of Web Data Mining Based on Cloud Computing”, IEEE, 2016.
[15] D. Bavarva Bhaskar , Dheeraj Kumar Singh, “Multimedia questions and answering using web data mining”, IEEE, 2015.
[16] Ying Han , Kejian Xia, “Data Preprocessing Method Based on User Characteristic of Interests for Web Log Mining”, IEEE, 2014.
[17] Quang yang, “10 Challenging problems in Data Mining research”, World Scientific, Vol. 5, No. 4, pp 597-604, 2006.
[18] L. Habin, K. Vlado, “Combining mining of web server logs and web content for classifying users’ navigation pattern and predicting users future request”, J. Data Knowledge Eng., Vol. 61, pp. 304–330, 2014.
[19] Dhanashree S. medhekar, “Heart Disease prediction System using Naïve Bayes”, IJERSTE, Vol. 2, No. 3, pp. 1-5, 2013.
[20] Arno J. Knobbe, “Multi-Relational Data Mining”, SIKS, pp 1-130, 2015.
[21] F.O. Isinkaye, Y.O. Folajimi, B.A. Ojokoh, “Recommendation Systems: Principles, methods and evaluation” ELSEVIER, Vol. 16, pp. 261-273, 2015.
[22] K.Reka, T.N.Ravi, "Hybrid Recommender Systems: Process, Challenges, Approaches and Metrics", International Journal of Computer Sciences and Engineering, Vol. 6, No. 5, pp. 1024-1033, 2018.
[23] S.N. Patil, S.M. Deshpande, Amol D. Potgantwar, "Product Recommendation using Multiple Filtering Mechanisms on Apache Spark", International Journal of Scientific Research in Network Security and Communication, Vol. 5, No. 3, pp. 76-83, 2017