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IARMMD: A Novel System for Incremental Association Rules Mining from Medical Documents

by Hany Mahgoub
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 1
Year of Publication: 2013
Authors: Hany Mahgoub
10.5120/10599-5299

Hany Mahgoub . IARMMD: A Novel System for Incremental Association Rules Mining from Medical Documents. International Journal of Computer Applications. 64, 1 ( February 2013), 28-35. DOI=10.5120/10599-5299

@article{ 10.5120/10599-5299,
author = { Hany Mahgoub },
title = { IARMMD: A Novel System for Incremental Association Rules Mining from Medical Documents },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 64 },
number = { 1 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 28-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number1/10599-5299/ },
doi = { 10.5120/10599-5299 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:15:16.149942+05:30
%A Hany Mahgoub
%T IARMMD: A Novel System for Incremental Association Rules Mining from Medical Documents
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 1
%P 28-35
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a novel system for Incremental Association Rules Mining from Medical Documents (IARMMD). The system concerns with maintenance of the discovered association rules and avoids redoing the mining process on whole documents during the updating process. The design of the system is based on concepts representation. It designed to develop our previous D-EART system. The IARMMD improves the updating process, and will lead to decrease the number of scanning and the execution time. The system consists of three phases that are Text Preprocessing, Incremental Association Rule Mining, and Visualization phase. Hash-based Incremental Association Rule Mining Algorithm (HIARM) is introduced in the mining phase. The algorithm employs the power of data structure called Hash Table. The performance of the algorithm is compared with both Apriori and FUP algorithms for the execution time and the evaluation of the extracted association rules. The results reveal that the number of extracted association rules in the IARMMD system is always less than that in Apriori-based and FUP-based systems. Furthermore, the execution time of HIARM algorithm is much better than Apriori and FUP algorithms in the updating process in all experimental cases.

References
  1. Agrawal, R. , Imielinski,T. and Swami, A. 1993. Mining association rules between Sets of items in large databases. In Proceedings of the ACMSIGMOD Int. Conf. on Management of Data, Washington, D. C.
  2. Han, J. , Cai, Y. and Cercone N. 1993. Data-driven Discovered of Quantitive Rules in Relational Databases," In Proc. of IEEE KDE Conference.
  3. Cheung, D. , Han, J. , Ng V. , and Wong, C. Y. 1996 Maintenance of discovered association rules in large databases: An incremental updating technique. In 12th IEEE International Conference on Data Engineering.
  4. Agrawal, R. , and Srikant, R. 1994. Fast algorithms for mining association rules," In Jorge B. Bocca, Matthias Jarke, and Carlo Zaniolo, editors, Proc. 20th Int. conf. of very Large Data Bases, VLDB, Santigo, Chile.
  5. Mahgoub, H. , Keshk, A. , Torkey, F. and Ismail N. 2010. An Efficient Online System of Concept Based Association Rules Mining," in Proc. 7th Int. Conf. on Informatics and Systems (INFOS 2010), Faculty of Computers and Information, Cairo University, Egypt.
  6. (2009) the PubMed website [Online]. Available: http://www. ncbi. nlm. nih. gov/pubmed/
  7. Park, J. S. , Chen, M. S. , and Yu, P. S. 1995. An effective hash based algorithm for mining association rules. In Proc. 1995 ACM-SIGMOD Int. Conf. on Management of Data, San Jose, CA, pp. 175-186.
  8. Cheung, D. , Han, J. , Ng, V. , and Wong, C. Y. 1996 Maintenance of discovered Knowledge: A Case in Multi-level Association Rules. In Proceedings of the 2nd Int. Conf. on Knowledge Discovery and Data Mining, pp. 307-310.
  9. Cheung, D. W. , Lee, S. D. and Kao B. 1997. A General incremental technique for maintaining discovered association rules" , In Proc. of the 5th Intl. Conf. on Database Systems for Advanced Applications (DASFAA'97), Melbourne, Australia.
  10. Lee, S. and Cheung, D. 1997. Maintenance of Discovered association rules. When to update? In Proc. of Research Issues on Data Mining and Knowledge Discovery, pp 51- 58.
  11. Chang, C. C. , Li, Y. C. and Lee, J. S. 2005. An efficient algorithm for incremental mining of association rules. In Proc. of the 15th Int. Workshop on research issues in data engineering: stream data mining and applications (RIDE-SDMA'05), IEEE.
  12. Toivonen, H. 1996. Sampling Large Databases for Association Rules. Proceeding of the 22th International conference on Very Large Data Bases.
  13. Thomas, S. , Bodagala, S. , Alsabti, K. , and Ranka, S. 1997. An efficient algorithm for the incremental updation of association rules in large databases. In Proceedings of the 3rd Intl. Conf. on Knowledge Discovery and Data Mining (KDD'97), New Port Beach, California, pp. 263-266.
  14. R. Feldman, Y. Aumann, and O. Lipshtat, "Borders: An efficient algorithm for association generation in dynamic databases", Journal, Intelligent Information System, 1990, pp. 61-73.
  15. T. P. Hong C. Y. Wang and Y. H. Tao, "A new incremental data mining algorithm using pre-large itemsets", Journal, Intelligent Data Analysis, Vol. 5, No. 2, pp. 111-129, 2001.
  16. Amornchewin, R. and Kreesuradej, W. 2007. Incremental association rule mining using promising frequent itemset algorithm. In Proceeding 6th International Conference on Information, Communications and Signal Processing, pp. 1-5.
  17. Amornchewin, R. and Kreesuradej, W. 2008. Probability-based incremental association rule discovery algorithm. The 2008 International Symposium on Computer Science and its Applications (CSA-08), Australia.
  18. R. Amornchewin and W. Kreesuradej, "Mining Dynamic Databases using Probability-Based Incremental Association Rule Discovery Algorithm", Journal of Universal Computer Science, vol. 15, no. 12 , 2009, pp. 2409-2428 .
  19. R. Amornchewin, "Probability-based Incremental association rules discovery algorithm with hashing Technique", Int. Journal of Machine Learning and Computing, vol. 1, no. 1, 2011, pp. 43-48.
  20. Zhu, Y. 2010. Improvement and Realization of Association Rules Mining Algorithm Based on FP-tree. 2nd International Conference on Information Science and Engineering (ICISE), China.
  21. J. Han, J. Pei, Y. Yin and R. Mao, "Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree", Data Mining and Knowledge Discovery, pp. 53–87, IEEE 2004.
  22. Han, J. , Pei, J. and Yin, Y. 2000. Mining frequent patterns without candidate generation. The ACM SIGMOD Int. Conference on Management of Data.
  23. Zeng, H. and Bangrong, S. 2010. An Improved Algorithm of FP - tree Growth Based on Mapping. International Conference on Computer Application and System Modeling (ICCASM).
  24. Jian-ping, L. , Ying, W. and Fan-ding, Y. 2010. Incremental-Mining algorithm Pre-FP in association rules based on FP-tree. Networking and Distributed Computing (ICNDC), First Int. Conference, IEEE.
  25. Lin, C. -W. , Hong, T. –P. , & Lu, W. –H. "The Pre-FUFP algorithm for incremental mining" Journal of Expert Systems with Applications, 36, 2009.
  26. Hong, T. P. , Lin, J. W. and Wu, Y. L. 2006. Maintenance of fast updated frequent pattern trees for record modification. The Int. Conference on Innovative Computing, Information and Control, pp. 570-573, IEEE.
  27. B. Nath, D K Bhattacharyya and A. Ghosh, "Discovering Association Rules from Incremental Datasets," Int. J. of Computer Science & Communication Vol. 1, No. 2, July-December 2010.
  28. Mahgoub, H. and Rösner, D. 2006. Mining association rules from unstructured documents. In Proc. 3rd Int. Conf. on Knowledge Mining, ICKM, Prague, Czech Republic, pp. 167-172.
  29. H. Mahgoub, D. Rösner, N. Ismail and F. Torkey, "A Text Mining Technique Using Association Rules Extraction" Int. J. of Computational Intelligence, Vol. 4, Nr. 1, 2007 WASE.
Index Terms

Computer Science
Information Sciences

Keywords

Knowledge Engineering Text mining Data mining Knowledge Mining Incremental Association Rules Mining

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