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Interactive Post Mining Association Rules using Cost Complexity Pruning and Ontologies KDD

by Ch. Raja Ramesh, K V V Ramana, K. Raghava Rao, C V Sastry
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 68 - Number 20
Year of Publication: 2013
Authors: Ch. Raja Ramesh, K V V Ramana, K. Raghava Rao, C V Sastry
10.5120/11694-6034

Ch. Raja Ramesh, K V V Ramana, K. Raghava Rao, C V Sastry . Interactive Post Mining Association Rules using Cost Complexity Pruning and Ontologies KDD. International Journal of Computer Applications. 68, 20 ( April 2013), 16-21. DOI=10.5120/11694-6034

@article{ 10.5120/11694-6034,
author = { Ch. Raja Ramesh, K V V Ramana, K. Raghava Rao, C V Sastry },
title = { Interactive Post Mining Association Rules using Cost Complexity Pruning and Ontologies KDD },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 68 },
number = { 20 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 16-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume68/number20/11694-6034/ },
doi = { 10.5120/11694-6034 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:28:23.285781+05:30
%A Ch. Raja Ramesh
%A K V V Ramana
%A K. Raghava Rao
%A C V Sastry
%T Interactive Post Mining Association Rules using Cost Complexity Pruning and Ontologies KDD
%J International Journal of Computer Applications
%@ 0975-8887
%V 68
%N 20
%P 16-21
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In data mining, association rule mining is very strong and limited by the huge amount of delivered rules, because of these so many problems facing to implementation. To overcome these drawbacks, several methods were proposed in the literature such as item sets concise, redundancy reduction, and post processing. Based on statistical information by using these methods the extracted rules may not be useful for the user. Thus, it is crucial to help the decision-controller with an efficient post processing step in order to reduce the number of rules. In this paper we have implemented a new interactive approach using cost complexity pruning and filter discovered rules, by using this approach we have to reduce the tree size with minimum number of error of validation set.

References
  1. R. Agrawal, T. Imielinski, and A. Swami, "Mining Association Rules between Sets of Items in Large Databases," Proc. ACM SIGMOD, pp. 207-216, 1993.
  2. U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Advances in Knowledge Discovery and Data Mining. AAAI/MITPress, 1996.
  3. A. Silberschatz and A. Tuzhilin, "What Makes Patterns Interesting in Knowledge Discovery Systems," IEEE Trans. Knowledge and Data Eng. vol. 8, no. 6, pp. 970-974, Dec. 1996.
  4. M. J. Zaki and M. Ogihara, "Theoretical Foundations of Association Rules," Proc. Workshop Research Issues in Data Mining and Knowledge Discovery (DMKD '98), pp. 1-8, June 1998.
  5. D. Burdick, M. Calimlim, J. Flannick, J. Gehrke, and T. Yiu, "Mafia: A Maximal Frequent Itemset Algorithm," IEEE Trans. Knowledge and Data Eng. , vol. 17, no. 11, pp. 1490-1504, Nov. 2005.
  6. J. Li, "On Optimal Rule Discovery," IEEE Trans. Knowledge and Data Eng. , vol. 18, no. 4, pp. 460-471, Apr. 2006.
  7. M. J. Zaki, "Generating Non-Redundant Association Rules," Proc. Int'l Conf. Knowledge Discovery and Data Mining, pp. 34-43, 2000.
  8. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, "Efficient Mining of Association Rules Using Closed Itemset Lattices," Information Systems, vol. 24, pp. 25-46, 1999.
  9. B. Baesens, S. Viaene, and J. Vanthienen, "Post-Processing of Association Rules," Proc. Workshop Post-Processing in Machine Learning and Data Mining: Interpretation, Visualization, Integration,and Related Topics with Sixth ACM SIGKDD, pp. 20-23, 2000.
  10. B. Liu, W. Hsu, K. Wang, and S. Chen, "Visually Aided Exploration of Interesting Association Rules," Proc. Pacific-Asia Conf. Knowledge iscovery and Data Mining (PAKDD), pp. 380-389, 1999.
  11. A. Maedche and S. Staab, "Ontology Learning for the Semantic Web," IEEE Intelligent Systems, vol. 16, no. 2, pp. 72-79, Mar. 2001.
  12. R. J. Bayardo, Jr. , R. Agrawal, and D. Gunopulos, "Constraint-Based Rule Mining in Large, Dense Databases," Proc. 15th Int'lConf. Data Eng. (ICDE '99), pp. 188-197, 1999.
  13. F. Guillet and H. Hamilton, Quality Measures in Data Mining. Springer, 2007.
  14. P. -N. Tan, V. Kumar, and J. Srivastava, "Selecting the Right Objective Measure for Association Analysis," Information Systems, vol. 29, pp. 293-313, 2004.
  15. G. Piatetsky-Shapiro and C. J. Matheus, "The Interestingness of Deviations," Proc. AAAI'94 Workshop Knowledge Discovery in Databases, pp. 25-36, 1994.
  16. M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo, "Finding Interesting Rules from Large Sets of Discovered Association Rules," Proc. Int'l Conf. Information and Knowledge Management (CIKM),
  17. E. Baralis and G. Psaila, "Designing Templates for Mining Association Rules," J. Intelligent Information Systems, vol. 9, pp. 7-32, 1997.
  18. B. Padmanabhan and A. Tuzhuilin, "Unexpectedness as a Measure of Interestingness in Knowledge Discovery," Proc. Workshop Information Technology and Systems (WITS), pp. 81-90,1997.
  19. T. Imielinski, A. Virmani, and A. Abdulghani, "Datamine: Application Programming Interface and Query Language for Database Mining," Proc. Int'l Conf. Knowledge Discovery and Data Mining (KDD), pp. 256-262, http://www. aaai. org/Papers/KDD/1996/KDD96-042. pdf, 1996.
  20. R. T. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang, "Exploratory Mining and Pruning Optimizations of Constrained Associations Rules," Proc. ACM SIGMOD Int'l Conf. Management of Data, vol. 27,pp. 13-24, 1998.
  21. A. An, S. Khan, and X. Huang, "Objective and Subjective Algorithms for Grouping Association Rules," Proc. Third IEEE Int'l Conf. Data Mining (ICDM '03), pp. 477-480, 2003.
  22. A. Berrado and G. C. Runger, "Using Meta rules to Organize and Group Discovered Association Rules," Data Mining and Knowledge Discovery, vol. 14, no. 3, pp. 409-431, 2007.
  23. M. Us hold and M. Gru¨ ninger, "Ontologies: Principles, Methods,and Applications," Knowledge Eng. Rev. , vol. 11, pp. 93-155, 1996.
  24. T. R. Gruber, "A Translation Approach to Portable Ontology Specifications," Knowledge Acquisition, vol. 5, pp. 199-220, 1993.
  25. N. Guarino, "Formal Ontology in Information Systems," Proc. FirstInt'l Conf. Formal Ontology in Information Systems, pp. 3-15, 1998.
  26. H. Nigro, S. G. Cisaro, and D. Xodo, Data Mining with Ontologies: Implementations, Findings and Frameworks. Idea Group, Inc. , 2007.
  27. V. Svatek and M. Tomeckova, "Roles of Medical Ontology inAssociation Mining Crisp-dm Cycle," Proc. Workshop Knowledge Discovery and Ontologies in ECML/PKDD, 2004.
  28. X. Zhou and J. Geller, "Raising, to Enhance Rule Mining in Web Marketing with the Use of an Ontology," Data Mining with Ontologies: Implementations, Findings and Frameworks, pp. 18-36,Idea Group Reference, 2007.
  29. M. A. Domingues and S. A. Rezende, "Using Taxonomies to Facilitate the Analysis of the Association Rules," Proc. Second Int'l Workshop Knowledge Discovery and Ontologies, held with ECML/PKDD, pp. 59-66, 2005.
  30. A. Bellandi, B. Furletti, V. Grossi, and A. Romei, "Ontology- Driven Association Rule Extraction: A Case Study," Proc. Workshop Context and Ontologies: Representation and Reasoning, pp. 1-10,2007.
  31. R. Natarajan and B. Shekar, "A Relatedness-Based Data-Driven Approach to Determination of Interestingness of AssociationRules," Proc. 2005 ACM Symp. Applied Computing (SAC), pp. 551-552, 2005.
  32. A. C. B. Garcia and A. S. Vivacqua, "Does Ontology Help Make Sense of a Complex World or Does It Create a Biased Interpretation?"Proc. Sensemaking Workshop in CHI '08 Conf. Human Factorsin Computing Systems, 2008.
  33. A. C. B. Garcia, I. Ferraz, and A. S. Vivacqua, "From Data to Knowledge Mining," Artificial Intelligence for Eng. Design, Analysis and Manufacturing, vol. 23, pp. 427-441, 2009.
  34. Claudia Marinica and Fabrice Guillet "Knowledge-Based Interactive Postmining of Association Rules Using Ontologies" eVOL. 22, NO. 6, JUN.
Index Terms

Computer Science
Information Sciences

Keywords

clustering classification and association rules knowledge discover database

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