Mining Association Rule of Frequent Itemsets Measures for an Educational Environment
Section:Research Paper, Product Type: Journal Paper
Volume-4 , Issue-7 , Page no. 8-17, Jul-2016
Online published on Jul 31, 2016
Copyright © N. Balajiraja . 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: N. Balajiraja, “Mining Association Rule of Frequent Itemsets Measures for an Educational Environment”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.7, pp.8-17, 2016.
MLA Style Citation: N. Balajiraja "Mining Association Rule of Frequent Itemsets Measures for an Educational Environment." International Journal of Computer Sciences and Engineering 4.7 (2016): 8-17.
APA Style Citation: N. Balajiraja, (2016). Mining Association Rule of Frequent Itemsets Measures for an Educational Environment. International Journal of Computer Sciences and Engineering, 4(7), 8-17.
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|This study deals with the design of an hostel inmate - informatics system, which addresses the issues to discover the fact likeness to stay. By using Data Mining (DM) techniques, the data stored in a Data Warehouse (DW) can be analyzed for the purpose of uncovering and predicting hidden patterns within the data. So far, different approaches have been proposed to accomplish the conceptual design of Data Warehouse by applying the multidimensional modeling paradigm. This paper presents a novel approach to integrating data mining model into multidimensional models in order to accomplish the conceptual design of Data Warehouse with Association Rules (AR). To this extent, the Association Rules for modeling in the conceptual level. The main advantage of our proposal is that the Association Rules rely on the goals and user requirements of the Data Warehouse, instead of the traditional method of specifying Association Rules by considering only the final database implementation structures such as tables, rows or columns. In this way to show the benefits of our approach, implementation of specified Association Rules would be created on a commercial database management server.|
|Key-Words / Index Term :|
|SData Mining ; Data Warehousing; Multidimensional; Association rule|
 Hamid Mohamadlou,”A method for mining association rules in quantitative and fuzzy data” ,978-1-4244-4136-5/09, IEEE, 2009, pp.453 – 458.
 Agrawal, R, and Srikant, R, “Fast algorithms for mining association rules” In: Proceedings of the 20th VLDB Int’l Conf., 1994, pp 487–499.
 Agrawal, R, and Srikant, R, “Fast algorithms for mining association rules in large database”, Technical report Fj9839, IBM Almaden Research Center, San jose, CA, jun.1994.
 Agrawal, R, and Imielinski, T, Swami, A, “Mining association rules between sets of items in large databases”, In Proc,ACM SIGMOD, May 1993,pp.207-216.
 XindongWu and et al, ” Top 10 algorithms in data mining”, Knowl Inf Syst ,Springer-Verlag London Limited , 2008, pp 14:1–37.
 Attila Gyenesei, "A Fuzzy approach for mining quantitative association rules", Technical Report: TUCS-TR-336, 2000.
 Zhu Ming, datamining, University of Science and Technology, china Press, Hefel, 2002, pp: 115 – 126.
 LI Pingxiang, CHEN Jiangping and BIAN Fuling, “A Developed Algorithm of Apriori Based on Association Analysis”, Geo-spatial Information Science ,Vol. 7, Issue 2, 2004, pp 108-112.
 HUANG Liusheng, CHEN Huaping, WANG Xun and CHEN Guoliang, “A Fast Algorithm for Mining Association Rules”, J. Comput. Sci. & Technol., Vol. 15 No. 6, 2000, pp 619-624,.
 Carlos Ordonez, Norberto Ezquerra and Cesar, A, Santana, “Constraining and summarizing association rules in medical data”, Knowl Inf Syst, 9(3), 2006, pp 259-283.
 Berry M.J.A and Linoff, G.S., “Data Mining Techniques for Marketing Sales and Customer Support”, John Wiley & Sons, Inc., 1997.
 Brin, S., Motwani, R., Ullman, J., and Tsur, S., “Dynamic itemset counting and implication rules for market basket data”,In Proc. of the ACM-SIGMOD Int’l Conf. on the Management of Data, 1997, PP. 255-264.
 Daniel Kunkle, Donghui Zhang, Gene Cooperman, “Mining Frequent Generalized Itemsets and Generalized Association Rules Without Redundancy”, J. Comput. Sci. & Technol., Vol. 23(1), 2008, pp. 77-102.
 Calvanesea, D., Dragoneb, L., Nardib, D., Rosatib, R., and Trisolinic, S.M., Enterprise modelling and data warehousing in TELECOM ITALIA. Information Systems 3, 2006.
 Comelli, M., Fe´nie` s, P., Gourgand, M., and Tchernev, N., “A generic evaluation model of logistic process for cash flow and activity based costing for a company supply chain”, In: International Conference on Industrial Engineering and Systems Management IESM 2005. Marrakech, Morocco, pp. 113–122, 2005.
 Caiyan Dai and Ling Chen, “An Algorithm for Mining Frequent Closed Itemsets with Density
from Data Streams”, IJCSE, pp. 40 – 48, 2016.