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.
|Correspondence should be addressed to: firstname.lastname@example.org.|
Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-11 , Page no. 44-49, 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.
|View this paper at Google Scholar | DPI Digital Library|
|XML View||PDF Download|
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.44-49, 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): 44-49.
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), 44-49.
|62||85 downloads||14 downloads|
|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|
 V. Palanisamy and A. Kumarkombaiya, "Designing a Knowledge Discovery of Clustering Techniques in Pharmaceutical Compounds", International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.58-63, 2015.
 Raghupathi W. “Data mining in healthcare. Healthcare Informatics: Improving Efficiency through Technology”, Analytics, and Management. 2016 Apr 27:353-72..
 Biafore, S. (1999). Predictive solutions bring more power to decision makers. Health Management Technology, Vol.20(10), pp 12-14.
 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.
 Siji PD, Valarmathi ML, Mohana S. A new soft computing technique for efficient rule mining. Journal of Theoretical and Applied Information Technology. 2016 Jan 1;83(3):434.
 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.
 Mishra N, Silakari DS. Predictive Analytics: A Survey, Trends, Applications, Oppurtunities & Challenges. International Journal of Computer Science and Information Technologies. 2012;3(3):4434-8.
 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.
 Kumari Nirmala, R.M.Singh and Shilpi Gupta, "Analysis for Heart Related Issues using comprehensive Approaches: A Review", International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.184-187, 2015.
 Nithya NS, Duraiswamy K. Gain ratio based fuzzy weighted association rule mining classifier for medical diagnostic interface. Sadhana. 2014 Feb 1;39(1):39-52.
 Kuok, C. M., Fu, A., & Wong, M. H. (1998). Mining fuzzy association rules indatabases. ACM SIGMOD Record, 27(1),pp- 41–46.
 M. Patel, A. Hasan , S.Kumar, "A Survey: Preventing Discovering Association Rules for Large Data Base", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.2, pp.30-32, 2013.
 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).
 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.
 Sowan B, Dahal K, Hossain MA, Zhang L, Spencer L. Fuzzy association rule mining approaches for enhancing prediction performance. Expert Systems with Applications. 2013 Dec 1;40(17):6928-37.
 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.