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Thus, the proposed work focuses on prevailing challenges in data analytics and its application on social media like Facebook, Twitter, blogs, e-commerce, e-service and so on. Among all of the possible interactions, e-commerce, e-education, and e-services have been identified as important domains for analytics techniques. So, it focuses on machine learning technique in improving practice and research in such e-X domains. Empirical analysis is done to show the performance of proposed system using real-time datasets.<\/jats:p>","DOI":"10.4018\/ijitwe.2020010106","type":"journal-article","created":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T13:29:02Z","timestamp":1572874142000},"page":"75-94","source":"Crossref","is-referenced-by-count":8,"title":["An Intelligent System for Predicting a User Access to a Web Based E-Learning System Using Web Mining"],"prefix":"10.4018","volume":"15","author":[{"family":"Sathiyamoorthi V.","sequence":"first","affiliation":[{"name":"Sona College of Technology, India"}]}],"member":"2432","reference":[{"key":"IJITWE.2020010106-0","doi-asserted-by":"crossref","unstructured":"Adami, G., Avesani, P., & Sona, D. (2003). Clustering Documents in a Web Directory. In Proceedings of WIDM\u201903 (pp. 66-73). 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