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Abstract

Web mining is the application of data mining techniques to gather useful information from the World Wide Web. The rapid increase in digital use makes web usage mining (a subtype of web mining) important. To tackle the issues in web usage mining, we introduce a combination of hierarchical user emotion analysis and a self-organizing mapping algorithm in the training and testing of a recommended system. This method identifies the least dissimilar element, which will not last, and prefers the highest priority element in the cluster. The quality of the proposed system is evaluated in terms of entropy, purity, and Davies-Bouldin index. The proposed method is compared with various traditional clustering approaches such as ant colony clustering, k-means clustering, and genetic algorithm. The experimental results show that our proposed system provides 40% better quality when compared with traditional clustering approaches.

Keywords

Data mining Web mining Web usage mining Recommended system User profiles 

References

  1. 1.
    Etzioni, The world wide web: quagmire or gold mine? Comm. ACM 39, 65–98 (1996)CrossRefGoogle Scholar
  2. 2.
    P. Kolari, A. Joshi, Web mining: research and practice. Comput. Sci. Eng. 6(4), 49–53 (2004)CrossRefGoogle Scholar
  3. 3.
    D. Tanasa, B. Trousse, Advanced data preprocessing for intersites web usage mining. IEEE Intell. Syst. 19(2), 59–65 (2004)CrossRefGoogle Scholar
  4. 4.
    O. Nasraoui, M. Soliman, E. Saka, A. Badia, R. Germain, A web usage mining framework for mining evolving user profiles in dynamic web sites. IEEE Trans. Knowl. Data Eng. 20(2), 202–215 (2008)CrossRefGoogle Scholar
  5. 5.
    S.G. Petridou, V.A. Koutsonikola, A.I. Vakali, G.I. Papadimitriou, Time aware web users clustering. IEEE Trans. Knowl. Data Eng. 20(5), 653–667 (2007)CrossRefGoogle Scholar
  6. 6.
    A. Kundu, E. Bertino, A new model for secure dissemination of XML content. IEEE Trans. Syst. Man. Cybern. Part C Appl. Rev. 38(3), 292–301 (2008)CrossRefGoogle Scholar
  7. 7.
    M.K. Agarwal, G. Kar, R. Mahindru, A. Neogi, A. Saile, Performance problem prediction in transaction-based e-business systems. IEEE Trans. Netw. Serv. Manag. 5(1), 1–10 (2008)CrossRefGoogle Scholar
  8. 8.
    D. Pierrakos, G. Paliouras, Personalizing web directories with the aid of web usage data. IEEE Trans. Knowl. Data Eng. 22(9), 1331–1344 (2010)CrossRefGoogle Scholar
  9. 9.
    M.H. Chehreghani, C. Lucas, M. Rahgozar, OInduced: an efficient algorithm for mining induced patterns from rooted ordered trees. IEEE Trans. Syst. Man. Cybern. Part A Syst. Hum. 41(5), 1013–1036 (2011)CrossRefGoogle Scholar
  10. 10.
    C.C. Chen, Z.Y. Chen, C.Y. Wu, An unsupervised approach for person name bipolarization using principal component analysis. IEEE Trans. Knowl. Data Eng. 24(11), 170–178 (2012)CrossRefGoogle Scholar
  11. 11.
    A. Abraham, V. Ramos, Web usage mining using artificial ant colony clustering and linear genetic programming. Congr. Evol. Comput. 2, 1384–1391 (2003)Google Scholar
  12. 12.
    M.M.V. Hulle, Self organising maps, in Handbook of Natural Computing, (Springer, Berlin, 2012), pp. 585–622CrossRefGoogle Scholar
  13. 13.
    P.N. Tan, M. Stenbach, V. Kumar, Introduction to Data Mining (Addison Wesley, Boston, 2005), pp. 1–6Google Scholar
  14. 14.
    D.L. Davies, D.W. Bouldin, Cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1, 95–104 (1979)Google Scholar
  15. 15.
    M. Alphy, A. Sharma, Study on online community user motif using web usage mining. J. Phys. Conf. Ser. 710, 012015 (2016)CrossRefGoogle Scholar
  16. 16.
    The Jester Dataset. https://grouplens.org/datasets/jester/. 07 / 07/ 2001

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Meera Alphy
    • 1
  • Ajay Sharma
    • 1
  1. 1.Department of Computer Science and EngineeringSRM University, Delhi-NCRSonipatIndia

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