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Extracting Semantically Similar Frequent Patterns Using Ontologies

  • S. Vasavi
  • S. Jayaprada
  • V. Srinivasa Rao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7077)

Abstract

Many methods were proposed to generate a large number of association rules efficiently. These methods are dependent on non-semantic information such as support, confidence. Also work on pattern analysis has been focused on frequent patterns, sequential patterns, closed patterns. Identifying semantic information and extracting semantically similar frequent patterns helps to interpret the meanings of the pattern and to further explore them at different levels of abstraction. This paper makes a study on existing semantic similarity measures and proposes a new measure for calculating semantic similarity using domain dependent and domain independent ontologies. This paper also proposes an algorithm SSFPOA (Semantically Similar Frequent Patterns extraction using Ontology Algorithm) for extracting and clustering semantically similar frequent patterns. The case study which is illustrated in this paper shows that the algorithm can be used to produce association rules at high level of abstraction.

Keywords

Frequent patterns Association rules Semantic similarity Ontology Clustering 

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References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association Trules. In: Proc. 20th Int’l Conf. on Very Large Databases, pp. 487–499 (1994)Google Scholar
  2. 2.
    Toivonen, H.: Sampling large databases for association rules. In: Int’l Conf. on Very Large Databases, pp. 134–145 (1996)Google Scholar
  3. 3.
    Srikant, R., Vu, Q., Agrawal, R.: Mining association rules with item constraints. In: Proc. of the 3rd Int’l Conf. on KDD and Data Mining (KDD 1997), Newport Beach, California (August 1997)Google Scholar
  4. 4.
    Yang, G., Shimada, K., Mabu, S., Hirasawa, K., Hu, J.: Mining Equalized Association Rules from Multi Concept Layers of Ontology Using Genetic Network Programming. In: Proc. of IEEE Cong. on Evolutionary Computation (CEC 2007), Singapore, pp. 705–712 (September 2007)Google Scholar
  5. 5.
    Yang, G., Shimada, K., Mabu, S., Hirasawa, K., Hu, J.: A Genetic Network Progrmming Based Method to Mine Generalized Association Rules with Ontology. Journal of Advanced Computational Intelligence and Intelligent Informatics (2006)Google Scholar
  6. 6.
    Mannila, H., Toivonen, H., Verkamo, A.I.: Efficient algorithms for discovering association rules. In: Proc. of the AAAI Workshop on Knowledge Discovery in Databases, pp. 181–192 (July 1994)Google Scholar
  7. 7.
    Shimada, K., Hirasawa, K., Hu, J.: Class Association Rule Mining with Chi-Squared Test Using Genetic Network Programming. In: Proc. of IEEE Int’l Conf. on Systems, Man and Cybernetics (ICSMC 2006), pp. 5338–5344 (October 2006)Google Scholar
  8. 8.
    Shimada, K., Hirasawa, K., Hu, J.: Genetic Network Programming with Acquisition Mechanisms of Association Rules. Journal of Advanced Computational Intelligence and Intelligent Informatics 10(1), 102–111 (2006)CrossRefGoogle Scholar
  9. 9.
    Agrawal, R., Imieliski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)Google Scholar
  10. 10.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53–87 (2004)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Mei, Q., Xin, D., Cheng, H., Han, J., Xiang, C.: Semantic Annotation of Frequent Patterns (2007)Google Scholar
  12. 12.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy (1995)Google Scholar
  13. 13.
  14. 14.
    Basu, S., Mooney, R.J., Pasupuleti, K.V., Ghosh, J.: Evaluating the Novelty of TextMined Rules Using Lexical Knowledge. In: Proceedings of the Seventh International Converence on Knowledge Discovery and Data Mining (2001)Google Scholar
  15. 15.
    Yang, G., Shimada, K., Mabu, S., Hirasawa, K.: A Personalized Association Rule Ranking Method Based on Semantic Similarity And Evolutionary Computation (2008)Google Scholar
  16. 16.
    Vasavi, S.: Semantic interoperability within heterogeneous environments using schema matching. PhD Thesis (2010)Google Scholar
  17. 17.
    Magnus, N., Hamilton, H.: package apriori, Copyright: University of Regina, Nathan Magnus and Su Yibin (June 2009)Google Scholar
  18. 18.
    Han, J., Fu, Y.: Mining Multiple-Level Association Rules in Large Databases. IEEE Transactions on Knowledge and Data Engineering 11(5) (September/October 1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • S. Vasavi
    • 1
  • S. Jayaprada
    • 1
  • V. Srinivasa Rao
    • 1
  1. 1.Computer Science & Engineering DepartmentVRSiddhartha Engineering College (Autonomous), Affliated to JNTU Kakinada, KANURUKrishna (DT)India

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