IJMTES – LEARNING BASED DYNAMIC QUERY FORM USING IMPROVED FUZZY C MEANS CLUSTERING

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : LEARNING BASED DYNAMIC QUERY FORM USING IMPROVED FUZZY C MEANS CLUSTERING

Author’s Name : Saranya S | Mrs N Santhana Krishna
unnamed

Volume 04 Issue 06 2017

ISSN no:  2348-3121

Page no: 86-88

Abstract – The abstract deals with user search goals for the mechanical keywords. The mechanical keywords are planned on the feedback sessions. The feedback sessions are outlined on the URL based logs and frequency. The analysis is performed on the fuzzy score to judge the performance. The economical approach are analyze on the logs of mechanically. The automatic bunches are planned on the feedback method. Semantic alike vocabulary on the significant factored to be atomized met data mining .Alternatively analyzed on the query logs. The knowledge formalized in global knowledge base constrains on the background knowledge.

Keywords – Fuzzy, Semantic User, Log Creation, Similarity

References

  1. C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for clustering evolving data streams. In Proceedings of VLDB, pages 81–92, Berlin, Germany, September 2003.
  2. R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong.Diversifying search results. In Proceedings of WSDM, pages 5–14, Barcelona, Spain, February 2009.
  3. S. Agrawal, S. Chaudhuri, G. Das, and A. Gionis.Automated ranking of database query results. In CIDR, 2003.
  4. S. Boriah, V. Chandola, and V. Kumar. Similarity measures for categorical data: A comparative evaluation. In Proceedings of SIAM International Conference on Data Mining (SDM 2008), pages 243–254, Atlanta, Georgia, USA, April 2008.
  5. G. Chatzopoulou, M. Eirinaki, and N. Polyzotis. Query recommendations for interactive database exploration. In Proceedings of SSDBM, pages 3–18, New Orleans, LA, USA, June 2009.
  6. S. Chaudhuri, G. Das, V. Hristidis, and G. Weikum.Probabilistic information retrieval approach for ranking of database query results. ACM Trans. Database Syst. (TODS), 31(3):1134– 1168, 2006.
  7. K. Chen, H. Chen, N. Conway, J. M. Hellerstein, and T. S. Parikh. Usher: Improving data quality with dynamic forms. In Proceedings of ICDE conference, pages 321–332, Long Beach, California, USA, March 2010.
  8. E. Chu, A. Baid, X. Chai, A. Doan, and J. F. Naughton. Combining keyword search and forms for ad hoc querying of databases. In Proceedings of ACM SIGMOD Conference, pages 349–360, Providence, Rhode Island, USA, June 2009.
  9. S. Cohen-Boulakia, O. Biton, S. Davidson, and C. Froidevaux.Bioguidesrs: querying multiple sources with a user-centric perspective. Bioinformatics, 23(10):1301–1303, 2007.
  10. G. Das and H. Mannila. Context-based similarity measures for categorical databases. In Proceedings of PKDD 2000, pages 201–210, Lyon, France, September 2000.
  11. W. B. Frakes and R. A. Baeza-Yates. Information Retrieval: Data Structures and Algorithms. Prentice-Hall, 1992.
  12. M. Jayapandian and H. V. Jagadish. Automated creation of a forms-based database query interface. In Proceedings of the VLDB Endowment, pages 695–709, August 2008.
  13. M. Jayapandian and H. V. Jagadish.Expressive query specification through form customization. In Proceedings of International Conference on Extending Database Technology (EDBT), pages 416–427, Nantes, France, March 2008.
  14. M. Jayapandian and H. V. Jagadish.Automating the design and construction of query forms. IEEE TKDE, 21(10):1389– 1402, 2009.
Scroll Up