An Integrated Approach and Framework for Document Clustering Using Graph Based Association Rule Mining

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)


Growth in number of documents increases day by day, and for managing this growth the document clustering techniques are used document clustering is a significant tool to allocating web search engines for data mining and knowledge discovery. In this paper, we have introduced a new framework graph-based frequent Term set for document clustering (GBFTDC). In this study, document clustering has been performed for extraction of useful information from document dataset based on frequent term set. We have generated association rules to perform pre-processing and then have applied clustering approach.


Document clustering Text document Association rule  Pre-processing 



This work is supported by research grant from MANIT, Bhopal, India under Grants in Aid Scheme 2010-11, No. Dean(R&C)/2010/63 dated 31/08/2010.


  1. 1.
    Kongthon, A.: A text mining framework for discovering technological intelligence to support science and technology management. Technical Report, Georgia Institute of Technology (2004)Google Scholar
  2. 2.
    Kalogeratos, A., Likas, A.: Document clustering using synthetic cluster prototypes. Data Knowl. Eng. 70, 284–306 (2011)Google Scholar
  3. 3.
    Fung, B., Wang, K., Ester, M.: Hierarchical document clustering using frequent itemsets. In: Proceeding of SIAM International Conference on Data Mining (SDM’03), pp. 59–70 (2003)Google Scholar
  4. 4.
    Michenerand, C.D., Sokal, R.R.: A quantitative approach to a problem in classification. Evolution 11, 130–162 (1957)Google Scholar
  5. 5.
    Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0 : Step-by-step data mining guide, NCR Systems Engineering Copenhagen (USA), DaimlerChrysler AG, SPSS Inc. (USA) and OHRA Verzekeringenen Bank Group B.V ( Netherlands), (2000)Google Scholar
  6. 6.
    Chen, C.L., Frank, S.C.T., Liang, T.: An integration of wordnet and fuzzy association rule mining for multi-label document clustering. Data Knowl. Eng. 69, 1208–1226 (2010)Google Scholar
  7. 7.
    Chen, C.L., Tseng, F.S.C., Liang, T.: An integration of fuzzy association rules and WordNet for document clustering. In: Proceeding of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-09), pp. 147–159 (2009)Google Scholar
  8. 8.
    Cutting, D.R., Karger, D.R., Pedersen, J.O., Tukey, J.W.: Scatter/Gather: A Cluster-based approach to browsing large document collections. In: Proceedings of the Fifteenth Annual International ACM SIGIR Conference, pp. 318–329, June 1992Google Scholar
  9. 9.
    Recupero, D.R.: A new unsupervised method for document clustering by using WordNet lexical and conceptual relations. Inf. Retrieval 10(6), 563–579 (2007)CrossRefGoogle Scholar
  10. 10.
    Rajput, D.S., Thakur, R.S., Thakur, G.S.: Rule generation from textual data by using graph based approach. In: International Journal of Computer Application (IJCA) 0975–8887, New york, ISBN: 978-93-80865-11-8, Vol. 31, No.9, pp. 36–43, Oct 2011Google Scholar
  11. 11.
    Dunham, M.H., Sridhar, S.: Data mining: introductory and advanced topics. Pearson Education, New Delhi, ISBN: 81-7758-785-4, 1st edn. (2006)Google Scholar
  12. 12.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Magazine, American Association for Artificial Intelligence (1996)Google Scholar
  13. 13.
    Beil, F., Ester, M., Xu, X.: Frequent term-based text clustering. In: Proceeding of International Conference on knowledge Discovery and Data Mining (KDD’02), pp. 436–442 (2002)Google Scholar
  14. 14.
    Fung, B.C.M., Wang, K., Ester, M.: Hierarchical document clustering using frequent itemsets. In: Proceedings of SIAM International Conference on Data Mining (2003)Google Scholar
  15. 15.
    Hammouda, K.M., Kamel, M.S.: Efficient phrase-based document indexing for web document clustering. IEEE Trans. Knowl. Data Eng. 16, 1279–1296 (2004)CrossRefGoogle Scholar
  16. 16.
    Han, I., Kamber, M.: Data Mining Concepts and Techniques, pp. 335–389. M. K. Publishers, Berlin (2000)Google Scholar
  17. 17.
    Haralampos, K., Christos, T., Babis, T.: An approach to text mining using information extraction. In: Proceeding Knowledge Management Theory Applications Workshop, (KMTA 2000), pp. 165–178. Lyon, Sept 2000Google Scholar
  18. 18.
    Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. Appl. Stat. 28, 126–130(1979)Google Scholar
  19. 19.
    Hotho, A., Staab, S., Stumme, G.: Wordnet improves text document clustering. In: Proceeding of SIGIR International Conference on Semantic Web, Workshop, (2003)Google Scholar
  20. 20.
    Hung, C., Xiaotie, D.: Efficient phrase-based document similarity for clustering. IEEE Trans. Knowl. Data Eng. 20, 1217–1229 (Sept 2008)Google Scholar
  21. 21.
    Introduction to Data Mining and Knowledge Discovery, 3rd edn. ISBN: 1-892095-02-5, Two Crows Corporation, 10500 Falls Road, Potomac, MD 20854, U.S.A., (1999)Google Scholar
  22. 22.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)Google Scholar
  23. 23.
    MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)Google Scholar
  24. 24.
    Jensen, C.S.: Introduction to Temporal Database Research.
  25. 25.
    Lovins, J.B.: Development of a stemming algorithm. Mech. Transl. Comput. Linguist. 11(1, 2), 22–31, June 1968Google Scholar
  26. 26.
    Kiran, G.V.R., Ravi Shankar, Vikram Pudi: Frequent itemset based hierarchical document clustering using wikipedia as external knowledge. KES 2010, Part II, LNAI 6277, pp. 11–20. Springer, Berlin (2010)Google Scholar
  27. 27.
    Lin, K., Kondadadi, R.: A word-based soft clustering algorithm for documents. In: Proceedings of Computers and Their Applications, pp. 391–394. Seattle (2001)Google Scholar
  28. 28.
    Larose, D.T.: Discovering knowledge in data: an introduction to data mining, Wiley, Inc., 2005. International Journal of Distributed and Parallel systems (IJDPS) Vol. 1, No. 1, (2010)Google Scholar
  29. 29.
    Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. KDD-2000 Workshop on Text Mining, pp. 109–110 (2000)Google Scholar
  30. 30.
    Rafi, Muhammad, Shahid Shaikh, M., Farooq, Amir: Document clustering based on topic maps. Int. J. Comput. Appl. 12(1), 32–36 (2010)Google Scholar
  31. 31.
    Nasukawa, T., Nagano, T.: Text analysis and knowledge mining system. IBM Syst. J. 40(4), 967–984 (2001)Google Scholar
  32. 32.
    Willett, P.: Recent trends in hierarchic document clustering: a critical review. Inf. Process. Manage. 24(5), 577–597 (1988)CrossRefGoogle Scholar
  33. 33.
    Lin, K., Kondadadi, R.: A word-based soft clustering algorithm for documents. In: Proceeding Computers and Their Applications, pp. 391–394 (2001)Google Scholar
  34. 34.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)Google Scholar
  35. 35.
    Richards, A.L., Holmans, P., O’Donovan, M.C., Owen, M.J., Jones, L.: A comparison of four clustering methods for brain expression microarray data. BMC Bioinform. 9, pp. 1–17 (2008)Google Scholar
  36. 36.
    Thakur, R.S., Jain, R.C., Pardasani, K.R.: Graph theoretic based algorithm for mining frequent patterns. In: IEEE World Congress on Computational Intelligence, pp. 629–633. Hong Kong (2008)Google Scholar
  37. 37.
    Thakur, R.S., Jain, R.C., Pardasani, K.R.: Fast algorithms for mining multi-level association rules in large databases. Asian J. Inf. Manage. USA 1(1), 19–26 (2008)Google Scholar
  38. 38.
    Thakur, R.S., Jain, R.C., Pardasani, K.R.: MAXFP: a multi-strategy algorithm for mining maximum frequent pattern and their support counts. Trends Appl. Sci. Res. 1(4), 402–415 (2006)CrossRefGoogle Scholar
  39. 39.
    Vishnu Priya, R., Vadivel, A., Thakur, R.S.: Frequent pattern mining using modified CP-Tree for knowledge discovery. Advanced Data Mining and Applications, LNCS-2010, Vol. 6440, pp. 254–261. Springer, Berlin (2010)Google Scholar
  40. 40.
    Soon, M.C., John, D.H., Yanjun, L.: Text document clustering based on frequent word meaning sequences. Data Knowl. Eng. 64, 381–404 (2008)CrossRefGoogle Scholar
  41. 41.
    Valentina, C., Sylvie, D.: Text mining supported terminology construction. In: Proceedings of the 5th International Conference on Knowledge Management, pp. 588–595. Graz, Austria (2005)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Computer ApplicationsM.A.N.I.T.BhopalIndia

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