International Journal of
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Data Mining Approach for Feature Reduction Using Fuzzy Association Rule
Data Mining Approach for Feature Reduction Using Fuzzy Association Rule
Siji P D1 , M.L.Valarmathi 2
1 Department of Computer science, St. Josephs College Irinjalakuda, Thrissur, India.
2 Department of EEE, Alagappa Chettiar College of Engineering and Technology College Road, Thilagar Nagar, India.
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Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-11 , Page no. 44-50, Nov-2017


Online published on Nov 30, 2017

Copyright © Siji P D, M.L.Valarmathi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Siji P D, M.L.Valarmathi, “Data Mining Approach for Feature Reduction Using Fuzzy Association Rule”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.44-50, 2017.

MLA Style Citation: Siji P D, M.L.Valarmathi "Data Mining Approach for Feature Reduction Using Fuzzy Association Rule." International Journal of Computer Sciences and Engineering 5.11 (2017): 44-50.

APA Style Citation: Siji P D, M.L.Valarmathi, (2017). Data Mining Approach for Feature Reduction Using Fuzzy Association Rule. International Journal of Computer Sciences and Engineering, 5(11), 44-50.
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Abstract :
Data mining is an upgrading technology for knowledge extraction in many fields like medical, educational, industrial, etc. Extracting an important data from large database is most vital factor. Data extraction processwere done through many techniques like feature extraction, prediction, classification, etc. for our research analyses prediction of data mining helps a lot for accessing useful information. In this paper we focused on road traffic dataset and we used fuzzy data extraction for membership function by using FCM. For the knowledge extraction process here we implemented the correlation and coefficient algorithm for road traffic dataset and attribute reduction were done by using Genetic algorithm and finally with the help of A-Priori algorithm we generate the rule for the mining the associate object for feature reduction.
Key-Words / Index Term :
Data Mining, Prediction, Feature Reduction, Fuzzy, Association Rule and Rule Generation
References :
[1] A survey of Knowledge Discovery and Data Mining process models The Knowledge Engineering Review, Vol. 21:1- 68 2006.
[2] Data Mining in Healthcare: Current Applications and Issues By Ruben D. Canlas Jr.Aug-2009.
[3] Biafore, S. (1999). Predictive solutions bring more power to decision makers. Health Management Technology, Vol.20(10), pp 12-14.
[4] Lu, J., Xu, B. & Jiang, J. (2003). A prediction method of fuzzy association rules. InProceedings of IEEE international conference on information reuse and integration(pp. 98–103), Nanjing, China.
[5] Siji P D, Dr.M.L.Valarmathi AndS.Mohana, “A new soft computing technique for efficientRule mining”
[6] Lei, Z. &Ren-hou, L. (2007). An algorithm for mining fuzzy association rules basedon immune principles. In Proceedings of the 7th IEEE international conference onbioinformatics and bioengineering, Boston, MA.
[7] Nishchol Mishra, Dr.SanjaySilakari, “Predictive Analytics: A Survey, Trends, Applications, Oppurtunities& Challenges”
[8] SunitaSoni and O.P.Vyas Using Associative Classifiers for Predictive Analysis in Health Care Data Mining ―International Journal of Computer Applications (0975 – 8887) Volume 4 – No.5, July 2010.
[9] Lilly P.L. and Siji P.D, “Fuzzy association rule mining approach withmodified clustering techniques for predictionperformance in medical database”
[10] N S NITHYA and K DURAISWAMY, “Gain ratio based fuzzy weighted association rule miningclassifier for medical diagnostic interface”.
[11] Kuok, C. M., Fu, A., & Wong, M. H. (1998). Mining fuzzy association rules indatabases. ACM SIGMOD Record, 27(1),pp- 41–46.
[12] Kanan, S., &Bhaskaran, R. (2009). Association rule pruning based on interestingness measures with clustering. International Journal of Computer Science Issues (IJCSI), 6(1), pp-35–43.
[13] Kaya, M. &Alhajj, R. (2003). Facilitating fuzzy association rules mining by usingmulti-objective genetic algorithms for automated clustering. In Proceedings ofthe third IEEE international conference on data mining (ICDM’03).
[14] Ho, G. T. S., Ip, W. H., Wu, C. H., &Tse, Y. K. (2012). Using a fuzzy association rulemining approach to identify the financial data association. Expert Systems withApplications, 39(10), 9054–9063. doi: 10.1016/j.eswa.2012.02.047.
[15] Bilal Sowan a, KeshavDahal a, M.A. Hossain b,⇑, Li Zhang b, Linda Spencer b, “Fuzzy association rule mining approaches for enhancing predictionperformance”
[16] Hong, T. P., Kuo, C. S., & Wang, S. L. (2004). A fuzzy AprioriTid mining algorithmwith reduced computational time. Applied Soft Computing Journal, 5(1), 1–10.