An Effective Model for Analysis Web User’s Behavior from Web Log Data

  • Authors

    • Jayanti Mehra
    • Dr. R.S. Thakur
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.17.21795
  • Fuzzy cluster validation, Probability Density Function, t location fit, User session clustering.

  • The regular explosion of e-Commerce, there is strong competition amongst companies and other sectors to be a focus for the customers. Web server analysis is very difficult to find out the web user behavior for any organization. It is useful for future web site improvement and design. In this paper, has to propose a PDFCM-based approach for assigning weights to client sessions for considering the high dimensionality of client session information. To analyzing the client session cluster, we use fuzzy c-means (FCM) algorithm. A major challenge for these methods is selection of suitable cluster center, so it has to propose PDFCM-based algorithm to solve this problem. Clustering is also used to approximation the number of clusters this clustering is similarly used to calculate the number of clusters. Our outcomes demonstrate that the quality of the clusters framed utilizing the proposed algorithm is much superior. So, outcome shows our proposed methodology is much batter then the other algorithms.

     

     

  • References

    1. [1] TT Aye, "Web log cleaning for mining of web us-age patterns."In: Proc. of 3rd International Conference on Computer Research and Development (ICCRD) IEEE, vol. 2, pp. 490-494, 2011.

      [2] Z Jiadi and G Hai, "Research on User Identification Algorithm based on Rewriting URL.", International Journal of Security and Its Applications ,Vol.10(3),pp. 215-222,2016.

      [3] A Kumar, V Ahirwar, RK Singh, â€A Study on Pre-diction of User Behavior Based on Web Server

      Log Files in Web Usage Mining’, International Journal Of Engineering And Computer Science, Vol. 6 (2), pp. 20233-20236,2015.

      [4] N Anand and S Hilal, "Identifying the User Access Pattern in Web Log Data", International Journal of Computer Science and Information Technologies, Vol.3 (2), pp. 3536-3539, 2012.

      [5] Neelima and S Rodda, "Predicting user behavior through sessions using the web log mining." In: Proc. of International Conference on Advances in Human Machine Interaction (HMI), 2016 pp. 1-5. IEEE, 2016.

      [6] T Vandana and M. K. Rao, "Linking Emotional In-telligence to Knowledge Sharing Behaviour: Or-ganizational Justice and Work Engagement as Me- diators", Global Business Review, Vol.18 (6), pp.1580-1596, 2017.

      [7] Z Ansari, SA Sattar, A.V. Babu, and M.F. Azeem, “Mountain density-based fuzzy approach for discovering web usage clusters from web log data, Fuzzy Sets and Systemsâ€, Vol.279(1), pp.40-63,2015.

      [8] B. Maheswari and P. Sumathi, "A New Clustering and Preprocessing for web log mining." In: Proc. of World Congress on Computing and Communication Technologies (WCCCT) IEEE, pp. 25-29, 2014.

      [9] A Gupta and A Khandekar, "Development of Web Log Mining Based On Improved Fuzzy C-Means Clustering Algorithm," International Journal of Science, Engineering and Technology Research (IJSETR), Vol.5 (3), March 2016.

      [10] N Pushpalatha and SSS Reddy, "Towards an extensible web usage mining framework for actionable knowledgeâ€, In: Proc of International Conference on Inventive Communication and Computational Technologies (ICICCT) IEEE, pp. 35-40, 2017.

      [11] V Anitha and P. Isakki, "A survey on predicting user behavior based on web server log files in a web usage mining", In: Proc. of International Conference on Computing Technologies and Intelli-gent Data Engineering (ICCTIDE) IEEE, pp. 1-4, 2016.

      [12] D Koutsoukos, G Alexandridis, G Siolas, and A Stafylopatis, "A new approach to session identifi-cation by applying fuzzy c-means clustering on web logs", In: Proc. of Symposium Series on In Computational Intelligence (SSCI) IEEE, pp. 1-8, 2016.

      [13] L Chaofeng, "Research on web session clustering", Journal of Software, Vol. 4(5), pp. 460-468, 2009.

      [14] A Zahid, AV Babuy, W Ahmed, and M F Azeemz, "A fuzzy set theoretic approach to discover user sessions from web navigational data", Recent Ad-vances in Intelligent Computational Systems (RA-ICS) IEEE, pp. 879-884, 2011.

      [15] B Chandra, M Gupta, and MP Gupta, "A multivariate time series clustering approach for crime trends prediction", In: Proc of International Con-ference on Systems, Man and Cybernetics, SMC IEEE., pp. 892-896, 2008.

      [16] MS Bhuvaneswari and K Muneeswaran, "Extracting usage patterns from web server log", In: Proc. of 2nd International Conference on Green High Performance Computing (ICGHPC) IEEE, pp.1-7, 2016.

      [17] P Sampath, and M. Prabhavathy, "Web page access prediction using fuzzy clustering by local approximation memberships (flame) algorithmâ€,ARPN Journal of Engineering and Applied Sciencesâ€, Vol. (10) 7, ISSN 1819-6608, 2015.

      [18] NP Gopalan, and J. Akilandeswari, "A distributed, fault-tolerant multi-agent web mining system for scalable web search",In: Proc. of WSEAS 5th In-ternational conference on Applied Informatics and Communications, pp. 15-7, 2005.

      [19] K Venkateswarlu, K Muni, A. Kandasamy, and K. Chandrasekaran. "An Energy-Efficient Hybrid Clustering Mechanism for Wireless Sensor Net-work," Unmanned Systems, Vol.3 (2), pp.109-125, 2015.

      [20] SK Jauha and M Pant, "Recent trends in supply chain management: A soft computing approach", In: Proc. of Seventh International Conference on Bio-Inspired Computing: Theories and Applica-tions (BIC-TA 2012) Springer, pp. 465-478, 2013.

      [21] C. Saravanan ,S Mishra , VK Dwivedi, and K. K. Pathak, "Discovering flood recession pattern inhydrological time series data mining during the post monsoon period", International Journal of Computer Applications, Vol. 90(8), 2014.

      [22] M Srivastava, R Garg, and P. K. Mishra, "Analysis of data extraction and data cleaning in Web usage mining", In: Proc .of International Conference on Advanced Research in Computer Science Engi-neering & Technology (ICARCSET 2015) ACM, pp. 13-13, 2015.

      [23] H Gulat, and P. K. Singh, "Clustering techniques in data mining: A comparison", In: Proc. of 2nd International Conference on Computing for Sus-tainable Global Development (INDIACom) IEEE, pp.410-415, 2015.

      [24] GS Chandel, K Patidar and MS Mali, "A Result Evolution Approach for Web usage mining using Fuzzy C-Mean Clustering Algorithm", In: Proc of International Journal of Computer Science and Network Security (IJCSNS), Vol.16(1),2016.

      [25] Ansari, Zahid, MF Azeem, AV Babu and W Ah-med, "A fuzzy approach for feature evaluation and dimensionality reduction to improve the quality of web usage mining resultsâ€, International Journal on Advanced Science Engineering Information Technology, Vol.2 (6), pp. 67-73, 2012.

      [26] D. P. Shrivastava, “Mining Frequent term set for Web Document Data using Genetic Algorithmâ€, International Journal of Pharmacy and Technology, Vol. 8(2), pp. 4038-4054, 2016.

      [27] L Wang and F Xiuju, "Rule extraction using a novel gradient-based method and data dimension-ality reduction", In:Proc. of International Joint Conference on Neural Networks, 2002. IJCNN'02 IEEE, Vol. 2, pp. 1275-1280, 2002.

      [28] K Das, G Ivan and MJN Arani, "Relations between distance–based and degree–based topological indices", Applied Mathematics and Computation, Vol. 270 (1), pp. 142-14, 2015.

      [29] Hu, Yating, Chuncheng Zuo, Yang Yang and Fuheng Qu, "A cluster validity index for fuzzy c-means clustering", In System Science, In:Proc. of International Conference on Engineering Design and Manufacturing Informatization (ICSEM) IEEE ,vol. 2, pp. 263-266 , 2011.

      [30] Weblog dataset downloaded from http://ita.ee.lbl .gov/html/contrib/NASA-HTTP.html (Retrieved on July 2015).

      [31] J Han and Kamber ,â€Data Mining: Concepts and Techniquesâ€, 3rd edition, 2011.

      [32] S. K. Dwivedi and B. Rawat, "A review paper on data preprocessing: A critical phase in web usage mining process", In: Proc. of International Conference on Green Computing and Internet of Things (ICGCIoT) IEEE, pp. 506-510, 2015.

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    Mehra, J., & R.S. Thakur, D. (2018). An Effective Model for Analysis Web User’s Behavior from Web Log Data. International Journal of Engineering & Technology, 7(4.17), 10-13. https://doi.org/10.14419/ijet.v7i4.17.21795