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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Classification Technique for Improving User Access on Web Log Data
verfasst von : Bina Kotiyal, Ankit Kumar, Bhaskar Pant, R. H. Goudar
Erschienen in: Intelligent Computing, Networking, and Informatics
Verlag: Springer India
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Abstract
In the present era, Internet is playing a significant role in our everyday life; therefore, it is very thorny to survive without it. Web log file that keeps track of the users’ access on net, if mined, can provide us precious information about the surfers. Similarly, the rapid growth of data mining applications has shown the necessity for machine learning algorithms to be applied to large-scale data. In this paper, we are using the naïve Bayesian (NB) classification technique using Weka for identifying the frequent access pattern. The main objective of this paper is to categorize browsing behavior of the user based on their position. This paper performs experiment and classifies the user access behavior from the large databases, which could result in increasing the efficiency and effectiveness of the system by reducing the browsing time of the user or results in fast retrieval of information from the system.