Abstract
Popularity of web increases day by day, everything associated with daily life of human being all are connected to web and most of the people spending their time over the web through social networking web apps for their purchasing. Web server stores every activity of users in the form of logs, which contain very useful patterns, henceforth web server log analysis is the vital research area. Web log data analysis has primary step is preprocessing which is meant for dimensionality reduction because web log data is hefty in size and need to normalize the data for further cognitive analysis or other data analysis. Bulky data size degrades the performance of data analytic algorithm so there is necessity of an efficient algorithm for preprocessing over web server log data. In this paper, we put emphasis on data preprocessing. We have proposed the use of genetic algorithm for dimension reduction and normalization of input web server log data. Experimental result shows the data preprocessed data produces higher precision value, precision calculated using MATLAB 2016 classification learner tool.
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Rao RJ, Stewart C, Perez A, Renganathan SM, Assessing learning behavior and cognitive bias from web logs. In: 2018 IEEE Frontiers in education conference (FIE). https://doi.org/10.1109/fie.2018.8658913
Vinay V, Wood K, Milic-Frayling N (2005) A comparison of dimensionality reduction techniques for text retrieval. In: Proceedings of the fourth international conference on machine learning and applications (ICMLA’05). 0-7695-2495-8/05 $20.00 ©
Mehra J, Thakur RS (2018) An Effective method for web log preprocessing and page access frequency using web usage mining. Int J Appl Eng Res 13(2):1227-1232. ISSN 0973-4562 © Research India Publications. http://www.ripublication.com
Xiang S, Zhong Z, Ding K (2015) Multicluster spatial—spectral unsupervised feature selection for hyperspectral image classification. IEEE
Miruthula P, Roopa SN (2015) Unsupervised feature selection algorithms: a survey. Int J Sci Res (IJSR)
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Kar, N.K., Mishra, M., Shrivastava, S.C. (2020). An Efficient Web Server Log Analysis Using Genetic Algorithm-Based Preprocessing. In: Saini, H., Sayal, R., Buyya, R., Aliseri, G. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 103. Springer, Singapore. https://doi.org/10.1007/978-981-15-2043-3_10
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DOI: https://doi.org/10.1007/978-981-15-2043-3_10
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