The week's pick
Random Articles
Reseach Article
Optimal Rule Selection Scheme using Concept Relationship Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 42 - Number 17 |
| Year of Publication: 2012 |
| Authors: R. Renuga Devi, R. Manavalan |
10.5120/5782-8015
|
R. Renuga Devi, R. Manavalan . Optimal Rule Selection Scheme using Concept Relationship Analysis. International Journal of Computer Applications. 42, 17 ( March 2012), 1-7. DOI=10.5120/5782-8015
Abstract
In Data Mining, the Association rule mining is used to retrieve the recurrent item sets. Apriori algorithm is mainly used to mine association rules. In that, rule reduction is required for efficient decision-making system. Knowledge based rule reduction schemes are used to filter the interested rules. In the existing system rule validation is not provided. Quantitative attributes are not considered in the post-mining scheme. Weighted rule mining scheme is not supported. This paper proposes Weighted Rule mining approach to perform post mining on derived rules with ontology support. Post mining schemes are used to filter consequent rules. Based on the Support and confidence values, the interested rules are selected rules and the same is used for the decision making process. Here, rule-mining scheme is improved to handle quantitative attributes. The WARM method is improved with validation methods. Then weighted rule mining and filtering process can be incorporated with the ARIPSO scheme. And also the rank based concept relationship analysis can be provided to improve the post mining process. Ontology based Association rule mining and Ontology based weighted Rule mining comparative analysis are focused.
References
- 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.
- M. J. Zaki, "Generating Non-Redundant Association Rules," Proc. Int'l Conf. Knowledge Discovery and Data Mining, pp. 34-43, 2000.
- J. Li, "On Optimal Rule Discovery," IEEE Trans. Knowledge and Data Eng. , vol. 18, no. 4, pp. 460-471, Apr. 2006.
- M. Hahsler, C. Buchta, and K. Hornik, "Selective Association Rule Generation," Computational Statistic, vol. 23, no. 2, pp. 303-315, Kluwer Academic Publishers, 2008.
- F. Guillet and H. Hamilton, Quality Measures in Data Mining. Springer, 2007.
- A. Berrado and G. C. Runger, "Using Metarules to Organize and Group Discovered Association Rules," Data Mining and Knowledge Discovery, vol. 14, no. 3, pp. 409-431, 2007.
- H. Nigro, S. G. Cisaro, and D. Xodo, Data Mining with Ontologies: Implementations, Findings and Frameworks. Idea Group, Inc. , 2007.
- 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.
- 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.
- 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 Factors in Computing Systems, 2008.
- 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.
- L. M. Garshol, "Metadata? Thesauri? Taxonomies? Topic Maps Making Sense of It All," J. Information Science, vol. 30, no. 4, pp. 378-391, 2004.
- I. Horrocks and P. F. Patel-Schneider, "A Proposal for an owl Rules Language," Proc. 13th Int'l Conf. World Wide Web, pp. 723-731, 2004.
- M. -A. Storey, N. F. Noy, M. Musen, C. Best, R. Fergerson, and N. Ernst, "Jambalaya: An Interactive Environment for Exploring Ontologies," Proc. Seventh Int'l Conf. Intelligent User Interfaces (IUI '02), pp. 239-239, 2002.
- Claudia Marinica and Fabrice Guillet "Knowledge-Based Interactive Postmining of Association Rules Using Ontologies" IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 6, June 2010.
Index Terms
Keywords