XML Data Analysis : Recent Review in Scope of Association Rule Generation

Authors

  • Vinod Prajapati  Information Technology, University Institute of Technology RGPV, Bhopal, Madhya Pradesh, India
  • Dr. Anjana Pandey  Information Technology, University Institute of Technology RGPV, Bhopal, Madhya Pradesh, India

Keywords:

Association Rule Mining, Semi Structures Data, XML, Graph, Tree, Frequent Pattern

Abstract

Revealing issues with current framework is itself a critical assignment. A review taken out for revealing issues related with Association standard mining on XML data. Preparatory essential ideas of Association rule mining is given in this work. Mining enormous amount of data, association rule mining have been demonstrated a powerful idea. Amid late years, the vast majority of the overall information exchanges is finished with XML (eXtensible Markup Language). Numerous empowering techniques have been distinguished and produced for mining XML data. In this paper, the idea of XML data examination is compressed and its importance towards association rule extraction has been represented. We have centered a variety of strategies and methodologies of the examination which are useful and set apart as the imperative field of XML data investigation. This work gives a study of different association rule strategies connected effectively on XML information since last one decade.

References

  1. Vivek T. and Thakur RS, "A level wise Tree Based Approach for Ontology-Driven Association Rules Mining", CiiT International Journal of Data Mining and Knowledge Engineering, Vol 4, No 5, 2012.
  2. Agrawal R. and Srikant. R, "Fast Algorithms for Mining Association Rules in Large Databases" Proceedings of the 20th International Conference on Very Large Data Bases, pp. 478-499, 1994.
  3. Larose, Daniel T. Discovering knowledge in data: an introduction to data mining. John Wiley & Sons, 2014.
  4. Vurukonda, G. Ranadheer, B. Mounika, and S. Reddy, "A Survey on Tree based Association Rules (TARs) from XML Documents", Proceedings of International Journal of Research and computational Technology, vol. 5, 2013.
  5. Mazuran, E. Quintarelli, and L. Tanca, "Data mining for XML Query- Answering Support", IEEE Transactions on Knowledge and data Engineering, vol.24, pp. 1393-1407, 2011.
  6. Wan and Gillian Dobbie. "Mining association rules from XML data using XQuery", In Proc. of the 2nd Ws on Australasian information security, Data Mining and Web Intelligence, and SW Internationalization, pp. 169-174, 2004.
  7. Han and M. Kamber, Data Mining: Concepts and Techniques. Morgan Kaufman Publisher, 2001.
  8. Al-Maolegi, Mohammed, and Bassam Arkok. "An improved apriori algorithm for association rules." arXiv preprint arXiv:1403.3948 ,2014.
  9. Yue XU, Gavin SHAW, Yuefeng LI, "Concise Representations for Association in Multilevel Datasets,"Systems Engineering Society of China & Springer-Verlag, vol.18 (1), pp.53-70, 2009.
  10. Vivek T. & Vipin T."Association Rule Mining- A Graph based approach for mining Frequent Itemsets" IEEE International Conference on Networking and Information Technology (ICNIT 2010) , pp. 309-313, Manila.
  11. Ding Q. and Gnanasekaran S., "Association Rule Mining from XML Data." In DMIN, pp. 144-152. 2006.
  12. Mazuran, M., Quintarelli, E., & Tanca, L., "Mining tree-based association rules from XML documents." Proceedings of the Seventeenth Italian Symposium on Advanced Database Systems ( SEBD), pp. 109-116. 2009.
  13. Thangarasu, S., and D. Sasikala. "Extracting Knowledge from XML Document Using Tree-Based Association Rules." Intelligent Computing Applications (ICICA), 2014 International Conference on. IEEE, 2014.
  14. Tiwari & Thakur RS, Contextual Snowflake Modeling for Pattern Warehouse Logical Design, Sadhana - Academy Proceedings in Engineering Science, Vol.40, Issue 1, PP. 15-33, 2015, Springer.
  15. Feng, T. S. Dillon, H. Weigand, and E. Chang. An xml-enabled association rule framework. In International Conference on Database and Expert Systems Applications (DEXA '12), pp. 88-97, 2012.
  16. Muralidhar, A. and Pattabiraman, V., An Efficient Association Rule Based Clustering of XML Documents, 2nd International Symposium on Big Data and Cloud Computing (ISBCC’15), Procedia Computer Science, vol. 50, pp.401-407, 2015, Elsevier.
  17. Kaur, G. and Aggarwal, N., Association Rule Mining in XML databases: Performance Evaluation and Analysis. International Journal of Computer Science and Technology (IJCST), Vol.1, Issue 2, 2010.
  18. Kolekar, S.V., Pai, R.M. and Pai, M.M., 2015, September. XML Based Pre-processing and Analysis of Log Data in Adaptive E-Learning System: An Algorithmic Approach. In International Conference on E-Learning, E-Education, and Online Training (pp. 135-143). Springer International Publishing, Springer, 2016.
  19. Swaraj, K.P. and Manjula, D., 2016. A fast approach to identify trending articles in hot topics from XML based big bibliographic datasets. Cluster Computing, Vol. 19, No. 2, pp.837-848, Springer , 2016.
  20. Piernik M, Brzezinski D, Morzy T. Clustering XML documents by patterns. Knowledge and Information Systems. Pp. 185-212, Vol. 46, Issue 1, Springer, 2016.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2016-12-30

Issue

Volume 2, Issue 6, November-December-2016

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Vinod Prajapati, Dr. Anjana Pandey, " XML Data Analysis : Recent Review in Scope of Association Rule Generation, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 6, pp.161-165, November-December-2016.