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Auto-Label Threshold Generation for Multiple Relational Classifications based on SOM Network

by Ram Prakash Gangwar, Jitendra Agrawal, Varsha Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 7
Year of Publication: 2012
Authors: Ram Prakash Gangwar, Jitendra Agrawal, Varsha Sharma
10.5120/4979-7237

Ram Prakash Gangwar, Jitendra Agrawal, Varsha Sharma . Auto-Label Threshold Generation for Multiple Relational Classifications based on SOM Network. International Journal of Computer Applications. 40, 7 ( February 2012), 38-42. DOI=10.5120/4979-7237

@article{ 10.5120/4979-7237,
author = { Ram Prakash Gangwar, Jitendra Agrawal, Varsha Sharma },
title = { Auto-Label Threshold Generation for Multiple Relational Classifications based on SOM Network },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 40 },
number = { 7 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number7/4979-7237/ },
doi = { 10.5120/4979-7237 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:27:28.329576+05:30
%A Ram Prakash Gangwar
%A Jitendra Agrawal
%A Varsha Sharma
%T Auto-Label Threshold Generation for Multiple Relational Classifications based on SOM Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 7
%P 38-42
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Classification and Association rule mining are two basic tasks of Data Mining. Classification rules mining finds rules that partition the data into disjoint sets. This paper is based on MrCAR (Multi-relational Classification Algorithm) and Kohonen’s Self-Organizing Maps (SOM) approach. SOM is a class of typical artificial neural networks (ANN) with supervised learning which has been widely used in classification tasks. For small disjunction mining, we collocate with a new auto level threshold generation method in our algorithm to solve the problem of unclassified data of MrCAR. So, we optimize the classification rate of MrCAR with SOM network and improve the efficiency of classification. This approach is highly effective for classification of various kinds of databases and has better average classification accuracy in comparison with MrCAR. Finally the results convincingly demonstrated that our proposed algorithm has high accuracy.

References
  1. Bing Liu, Wynne Hsu, Yiming Ma,“Integratin Classification and Association Rule Mining” Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), New York, USA, 1998, pp 80-86.
  2. Rakesh Agrawal, Tomasz Imieli_ski, and Arun Swami, “Mining Association Rules Between Sets of Items in Large Databases”, ACM SIGMOD Conference, New York, USA, 1993, pp 207-216.
  3. Rakesh Agrawal, and Ramakrishnan Srikant, “Fast algorithms for mining association rules in large databases”, Proceedings of the 20th International Conference on Very Large Data Bases(VLDB), Santiago, Chile, September 1994, pp 487-499.
  4. G. Dong, X. Zhang, L. Wong, and J. Li, “CAEP: Classification by aggregating emerging patterns”, Proceedings of the 2nd International Conference on Discovery Science, Springer-Verlag, Berlin Heidelberg, 1999, pp. 30-42.
  5. K. Wang, S. Zhou, and Y. He, “Growing decision trees on support-less association rules”, Proceedings of the KDD, ACM, Boston Massachusetts, 2000, pp. 265-269.
  6. W. Li, J. Han, and J. Pei, “CMAR: Accurate and efficient Classification Based on Multiple Class-Association Rules”, Proceedings of the ICDM, IEEE Computer Society, San Jose California, 2001, pp. 369-376.
  7. X. Yin, and J. Han, “CPAR: Classification based on Predictive Association Rules”, Proceedings of the SDM, SIAM, Francisco California, 2003.
  8. Robert C. Holte and Liane E. Acker and Bruce W. Porter., “Concept Learning and the Problem of Small Disjuncts” In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, 1989, pp 813-818.
  9. t. Kohonen. Self-organizing maps. Springer, Berlin, Heidelberg, 2001. Third extended edition, ISBN 3-540-67921-9.
  10. w. Li, “classification based on multiple association rules,” m.sc. Thesis, Simon Fraser University, April 2001.
  11. D.E .Rumelhart, G.E .Hinton, and R.J. Williams ?learning Internal representation by error prorogation,?inparallelDistributedprocessing.vol.1.:Foundations,D.E.Rumelhart,J.L.McClelland and the PDP research group,Eds.Cambridge,Mass.:MIT Press,1986,pp.318-362
  12. J.J.Hopfield, ?Neural network and physical systems with emergent collective computational activities,?Proc.Natl.Acad.Sci.USA,vol.79,pp.2554-2558,1982.
  13. Kohonen, T. Self Organizing Maps; Springer Series in Information Sciences Springer: Espoo, Finland, 1994.
  14. M.J. Zaki, and C.J. Hsiao, “CHARM: An Efficient Algorithm for Closed Itemset Mining”, Proceedings of SIAMOD International Conference on Data Mining, 2002, pp. 457-473.
  15. M.J. Zaki, and K. Gouda, “Fast Vertical Mining Using Diffsets”, Proceedings of the 9th ACM SIGKDD, ACM, New York USA, 2003, pp. 326-335.
  16. Jiawei Han, and Micheline Kamber, Data Mining: Concepts and Techniques, Second Edition, China Machine Press, Beijing, 2007. 260
  17. Yingqin Gu, Hongyan Liu, Jun He, Bo Hu and Xiaoyong Du,”MrCAR: A Multi-relational Classification Algorithm based on Association Rules”, 2009 International Conference on Web Information Systems and Mining.
  18. Sa?so D?zeroski,” MultiRelational Data Mining: An Introduction” Jamova 39, SI1000 Ljubljana, Slovenia
  19. pei-yi hao, yu-de chen,” a novel associative classification algorithm: a Combination of lac and cmar with new measure of Weighted effect of each rule group”, Proceedings of the 2011 International Conference on Machine Learning and Cybernetics, Guilin, 10-13 July, 2011
Index Terms

Computer Science
Information Sciences

Keywords

Classification Data mining MrCAR and SOM

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