Abstract
Web usage mining is one of the essential framework to find domain knowledge from interaction of users with the web. This domain knowledge is used for effective management of predictive websites, creation of adaptive websites, enhancing business and web services, personalization, and so on. In nonprofitable organization’s website it is difficult to identify who are users, what information they need, and their interests change with time. Web usage mining based on log data provides a solution to this problem. The proposed work focuses on web log data preprocessing, sparse matrix construction based on web navigation of each user and clustering the users of similar interests. The performance of web usage mining is also compared based on k-means, X-means and farthest first clustering algorithms.
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We would like to thank our college Sree Vidyanikethan Engineering college, Tirupathi for providing valuable resources and encouragement to do research.
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Dhana Lakshmi, P., Ramani, K., Eswara Reddy, B. (2017). Efficient Techniques for Clustering of Users on Web Log Data. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-10-3874-7_35
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DOI: https://doi.org/10.1007/978-981-10-3874-7_35
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