Web Log Mining to Enhance Surfing Experience


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Analyzing navigation history of a web site and that of a user reported in log files and web click stream helps in determining the components that shall be needed in the near future and fetch them in advance. Identifying the frequently visited websites from such data enhances the process. Processing and analyzing the vast amounts of click stream data and web logs that are generated at very high rates in a smart and cost-efficient way is a daunting challenge to the data mining community. Mining frequent itemsets plays an important role in analyzing such data streams. In spite of the existence of many such mining algorithms, more time and space efficient algorithms for mining frequent patterns are the need of the hour and are attracting wide attention in web click-stream analysis in recent years. In this paper an effective algorithm for mining frequent itemsets from a time-sensitive sliding window is proposed. It is a one-pass algorithm that uses a circular queue to implement the sliding window. The time-sensitive sliding window stores the web click stream data of various sessions of the web users. The proposed one-pass algorithm, FIM_CQTimeSWin has three phases: representation of time unit, maintenance of the sliding window and generation of frequent itemsets in the current sliding window.



Edited by:

R. Edwin Raj, M. Marsaline Beno and M. Carolin Mabel




A. Mala and D. F. Ramesh, "Web Log Mining to Enhance Surfing Experience", Applied Mechanics and Materials, Vol. 626, pp. 7-13, 2014

Online since:

August 2014




* - Corresponding Author

[1] J. Chang, W. Lee, A sliding window method for finding recently frequent itemsets over online data streams, J. of Information Science and Engineering, 20(4) (2004) 753–762.

[2] Mahmood Deypir, Mohammad Hadi Sadreddini,. An Efficient Algorithm for Mining Frequent Itemsets Within Large Windows over Data Streams, International Journal of Data Engineering, Volume (2) : Issue (3) (2011) 119-125.

DOI: https://doi.org/10.1109/iccke.2011.6413356

[3] Mahmood Deypir, Mohammad Hadi Sadreddini, A dynamic layout of sliding window for frequent itemset mining over data streams, Journal of Systems and Software archive Volume 85 Issue 3 (2012) 746-759.

DOI: https://doi.org/10.1016/j.jss.2011.09.055

[4] C. Giannella, J. Han, J. Pei, X. Yan & P.S. Yu, Mining frequent patterns in data streams at multiple time granularities, In H. Kargupta, A. Joshi, K. Sivakumar, & Y. Yesha (Eds. ), Data mining: Next generation challenges and future directions. AAAI/ MIT (2003).

DOI: https://doi.org/10.1111/j.1541-0420.2006.00540_16.x

[5] Y.S. Regant, Hung, Lap-Kei Lee, H.F. Ting, Finding frequent items over sliding windows with constant update time, Elsevier Information Processing Letter, 110 (2010) 257-260.

[6] Chao-Wei Li, Kuen-Fang Jea, An adaptive approximation method to discover frequent itemsets over sliding-window-based data streams, Expert Systems with Applications: An International Journal archive Volume 38 Issue 10 (2011) 13386-13404.

DOI: https://doi.org/10.1016/j.eswa.2011.04.167

[7] Chao-Wei Li, Kuen-Fang Jea, Ru-Ping Lin, Ssu-Fan Yen, Chih-Wei Hsu, Mining frequent patterns from dynamic data streams with data load Management, The Journal of Systems and Software 85 (2012)1346– 1362.

DOI: https://doi.org/10.1016/j.jss.2012.01.024

[8] G.S. Manku, R. Motwani, Approximate frequency counts over data streams, In Proceedings of the VLDB (2002) 346–357.

DOI: https://doi.org/10.1016/b978-155860869-6/50038-x

[9] Z. Qu, P. Li, Y. Li, A High-efficiency Algorithm for Mining Frequent Itemsets over Transaction Data Streams, In proceedings of the International Conference on Intelligent Control and Information Processing August 13-15, Dalian, China (2010).

[10] J. X. Yu, Z. Chong, H. Lu, Z. Zhang, A. Zhou, A false negative approach to mining frequent itemsets from high speed transactional data streams, Information Sciences, 176(14) (2006) 1986 –(2015).

DOI: https://doi.org/10.1016/j.ins.2005.11.003