A Novel Modified Apriori Approach for Web Document Clustering

  • Rajendra Kumar Roul
  • Saransh Varshneya
  • Ashu Kalra
  • Sanjay Kumar Sahay
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)


The Traditional apriori algorithm can be used for clustering the web documents based on the association technique of data mining. But this algorithm has several limitations due to repeated database scans and its weak association rule analysis. In modern world of large databases, efficiency of traditional apriori algorithm would reduce manifolds. In this paper, we proposed a new modified apriori approach by cutting down the repeated database scans and improving association analysis of traditional apriori algorithm to cluster the web documents. Further we improve those clusters by applying Fuzzy C-Means (FCM), K-Means and Vector Space Model (VSM) techniques separately. We use Classic3 and Classic4 datasets of Cornell University having more than 10,000 documents and run both traditional apriori and our modified apriori approach on it. Experimental results show that our approach outperforms the traditional apriori algorithm in terms of database scan and improvement on association of analysis.


Apriori Association K-means Fuzzy C-means VSM 


  1. 1.
    Barsagade, N.: Web usage mining and pattern discovery: asurvey paper. In: CSE8331, Dec 2003Google Scholar
  2. 2.
    Kumar, T.S.: Introduction to Data Mining. Pearson Education, Upper Saddle River (2006)Google Scholar
  3. 3.
    Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., InkeriVerkamo, A.: Fast discovery of association rules in large databases. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI press, Menlo Park (1996)Google Scholar
  4. 4.
    Wang, P., Shi, L., Bai, J., Zhao, Y.: Mining association rules based on apriori algorithm and application. In: International Forum on Computer Science-Technology and Applications, IFCSTA’09, vol. 1 (2009)Google Scholar
  5. 5.
    Bodon, F.: A trie-based APRIORI implementation for mining frequent itemset. In: ACM 1-59593-210-0/05/08 (2005)Google Scholar
  6. 6.
    Cao, X.: An algorithm of mining association rules based on granular computing. In: International Conference on Medical Physics and Biomedical Engineering (2012)Google Scholar
  7. 7.
    Wang, Y., Jin, Y., Li, Y., Geng, K.: Data mining based on improved apriori algorithm. Commun. Comput. Inf. Sci. 392, 354–363 (2013)CrossRefGoogle Scholar
  8. 8.
    Li, X., Shang, J.: A novel apriori algorithm based on cross linker. In: Proceedings of the International Conference on Information Engineering and Applications (IEA) (2012)Google Scholar
  9. 9.
    Tomanová, I., Kupka, J.: Implementation of background knowledge and properties induced by fuzzy confirmation measures in apriori algorithm. In: International Joint Conference CISIS’12-ICEUTE’12-SOCO’, vol. 189, pp. 533–542 (2013)Google Scholar
  10. 10.
    Li, Y., Xing, J., Wu, R., Zheng, F.: Web clustering using a two-layer approach. LNCS, vol. 6988, pp. 211–218. Springer, Heidelberg (2011)Google Scholar
  11. 11.
    Huang, F., Zhang, S., He, M., Wu, X.: Clustering web documents using hierarchical representation with multi-granularity. World Wide Web 17, 105–126 (2014). doi: 10.1007/s11280-012-0197-x CrossRefGoogle Scholar
  12. 12.
    Roul, R.K., Devanand O.R., Sahay S.K.: Web document clustering and ranking using Tf-Idf based Apriori Approach. In: IJCA Proceedings on International Conference on Advances in Computer Engineering and Applications, pp. 34–39 (2014)Google Scholar
  13. 13.
    Lee, I., On, B.W.: An effective web document clustering algorithm based on bisection and merge. Artif. Intell. 36, 69–85 (2011). doi: 10.1007/s10462-011-9203-4 CrossRefGoogle Scholar
  14. 14.
  15. 15.
    Orlando, S., Palmerini, P., Perego, R.: Enhancing the apriori algorithm for frequent set counting. LNCS, vol. 2114, pp. 71–82. Springer, Heidelberg (2001)Google Scholar
  16. 16.
    Singh, J., Ram, H., Sodhi, J.S.: Improving efficiency of apriori algorithm using transaction reduction. Int. J. Sci. Res. Publ. 3(1), 1–4 (2013)Google Scholar
  17. 17.

Copyright information

© Springer India 2015

Authors and Affiliations

  • Rajendra Kumar Roul
    • 1
  • Saransh Varshneya
    • 1
  • Ashu Kalra
    • 1
  • Sanjay Kumar Sahay
    • 1
  1. 1.BITS Pilani K. K. Birla Goa CampusZuarinagarIndia

Personalised recommendations