Email this article 
Open Access
Subscription or Fee Access
Students Placement Competency Analysis - An Apriori Framework
Abstract
Data Mining is an emerging research area, where the goal is to extract significant patterns or interesting rules from large databases. Association rule mining is a famous technique in data mining. The term association rule was first introduced by Agrawal et al. Association rules of are also referred in the literature as classical or boolean association rules. Association rules mining or induction is commonly used in market basket analysis to find items frequently bought together by shoppers. Pattern recognition aims to classify data based either on apriori knowledge or on statistical information extracted from the patterns. This paper aims to find the correlation among attributes for mining rules from student’s database. The dataset contains details about placement attributes, students skillset,academic qualification, language competency and personal information. In this work we have implemented the association rule mining algorithm a data mining task the apriori algorithm to find interesting rules. The study is implemented in weka tool to analyze
students competency factors related to placement.
Keywords
References
Arun K. Pujari, “Data Mining Techniques”, Universities Press (India)Limited 2001, ISBN 81-7371-3804
Markus he gland “The association rule of Apriori Algorithm” Departmentof Computer Science, volume 3456 pp 908.
Margaret H. Dunham, Yongqiao Xiao, Le Gruenwald, Zahid Hossain,“A Survey of Association Rules” Department of Computer Science and Engineering.
Christian Borgelt and Ruklot Kruse “Induction of Association Rules:apriori implementation” Department of knowledge processing and language Engineering, School of Computer Science, D-39106.
Hipp, J., Guntzer, U. and Nakhaeizadeh, G”. Algorithms for AssociationRule Mining – A General Survey and Comparison”. SIGKDD Explorations, (1). 58-64.
Ng, R. T., Lakshmanan, S., L. V., Han, J. and Pang, A., Exploratory Mining and Pruning Optimisations of Constrained Association Rules. In ACM SIGMOD, (Seattle, Washington, 1998), 13-24.
Agarwal R. Imielinksi T, Swami A, 1993, “Mining Association rules between set of items in large databases”. Proc, 1993 ACM SIGMOD Int.Conf on Management of Data (Washington, DC: ACM Press), PP 207-
Badr, Al-Daihani, “Hetreogenous Association Rules Mining” School of computer Science, Cardiff University, Queen’s Buildings, New Port Road
Bandyopadhyay, S., and Muthy, C. A. : “Pattern Classification using Genetic algorithms, Pattern Recognition Letters”, Vol. 16 (1995) 801-808.
Benson, D. A., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J and D. L Healer (2003) “GenBank. Nucleic Acids”, Res., 31, 23-27.
Buchter, O. and Wirth, R. Exploration of Ordinal Data using Association Rules, Knowledge and Information Systems, 1(4).
David Rundqvist “Model for Grouping Biological Data” LITHIDA-EX-06/029E, 2006-04-21.
J. Han, M. Kamber, Data Mining: Concepts and Techniques, 2006.
Judice L. Y. Koh, Momg Li Lee, Paul T. J. Tan, Vladimir Brusic,”Duplicate detection in Biological data using Association Rule Mining”,2nd Workshop on data mining and Text Mining for Bioinformatics, Pisa,Italy 2004.
Katherine G. Herbert, Narain H. Gehani, William H Piel, “ BIOAJAX - An Extensible Framework for Biological Data Cleaning “
Derived largely from S. Y. Chung and J. C. Wooley, “Challenges Faced in the Integration of Biological Information, ” Bioinformatics: Managing Scientific Data, Z. Lacroix and T. Critchlow, eds., Morgan Kaufmann,San Francisco, CA, 2003.
Nan Jiang and Le Gruenwald, ‘Research Issues in Data Stream Association Rule Mining’ SIGMOD Record, Vol. 35, No. 1, Mar. 2006
Markus he gland “The association rule of Apriori Algorithm” department of Computer Science, Volume 3456 pp 908.
Mikhail Blano and Raymond J. Moony “Data Cleaning and integeration process” Department of Electrical Engineering Systems.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.