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Combining Apriori Approach with Support-Based Count Technique to Cluster the Web Documents

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 556)

Abstract

The dynamic Web where thousands of pages are updated in every second is growing at lightning speed. Hence, getting required Web documents in a fraction of time is becoming a challenging task for the present search engine. Clustering, which is an important technique of data mining can shed light on this problem. Association technique of data mining plays a vital role in clustering the Web documents. This paper is an effort in that direction where the following techniques have been proposed:
  1. (1)

    a new feature selection technique named term-term correlation has been introduced which reduces the size of the corpus by eliminating noise and redundant features.

     
  2. (2)

    a novel technique named Support Based Count (SBC) has been proposed which combines with traditional Apriori approach for clustering the Web documents.

     

Empirical results on two benchmark datasets show that the proposed approach is more promising compared to the traditional clustering approaches.

Keywords

Apriori Cluster Fuzzy K-means Support based count 

References

  1. 1.
    A. Spink, D. Wolfram, M. B. Jansen, and T. Saracevic, “Searching the web: The public and their queries,” Journal of the American society for information science and technology, vol. 52, no. 3, pp. 226–234, 2001.Google Scholar
  2. 2.
    W. B. Croft, “A model of cluster searching based on classification,” Information systems, vol. 5, no. 3, pp. 189–195, 1980.Google Scholar
  3. 3.
    J. Tang, “Improved k-means clustering algorithm based on user tag,” Journal of Convergence Information Technology, vol. 12, pp. 124–130, 2010.Google Scholar
  4. 4.
    C. X. Lin, Y. Yu, J. Han, and B. Liu, “Hierarchical web-page clustering via in-page and cross-page link structures,” in Advances in Knowledge Discovery and Data Mining. Springer, 2010, pp. 222–229.Google Scholar
  5. 5.
    X. Gu, X. Wang, R. Li, K. Wen, Y. Yang, and W. Xiao, “A new vector space model exploiting semantic correlations of social annotations for web page clustering,” in Web-Age Information Management. Springer, 2011, pp. 106–117.Google Scholar
  6. 6.
    P. Worawitphinyo, X. Gao, and S. Jabeen, “Improving suffix tree clustering with new ranking and similarity measures,” in Advanced Data Mining and Applications. Springer, 2011, pp. 55–68.Google Scholar
  7. 7.
    M. T. Hassan and A. Karim, “Clustering and understanding documents via discrimination information maximization,” in Advances in Knowledge Discovery and Data Mining. Springer, 2012, pp. 566–577.Google Scholar
  8. 8.
    P. Li, B. Wang, and W. Jin, “Improving web document clustering through employing user-related tag expansion techniques,” Journal of Computer Science and Technology, vol. 27, no. 3, pp. 554–566, 2012.Google Scholar
  9. 9.
    R. K. Roul, S. Varshneya, A. Kalra, and S. K. Sahay, “A novel modified apriori approach for web document clustering,” in Computational Intelligence in Data Mining-Volume 3. Springer, 2015, pp. 159–171.Google Scholar
  10. 10.
    A. Inokuchi, T. Washio, and H. Motoda, “An apriori-based algorithm for mining frequent substructures from graph data,” in Principles of Data Mining and Knowledge Discovery. Springer, 2000, pp. 13–23.Google Scholar
  11. 11.
    M. Steinbach, G. Karypis, V. Kumar et al., “A comparison of document clustering techniques,” in KDD workshop on text mining, vol. 400, no. 1. Boston, 2000, pp. 525–526.Google Scholar
  12. 12.
    G. Salton, A. Wong, and C.-S. Yang, “A vector space model for automatic indexing,” Communications of the ACM, vol. 18, no. 11, pp. 613–620, 1975.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.BITS-PilaniSancoaleIndia

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