V. Vivekanandan, N. Karpagavalli, R. Manoj Kumar, A. Devipriya, SriGuru Institute of Technology, Coimbatore, Tamilnadu, India.
DOI : 01.0401/ijaict.2014.08.11
International Journal of Advanced Information and Communication Technology
Received On : June 10, 2017
Revised On : July 12, 2017
Accepted On : August 12, 2017
Published On : September 05, 2017
Volume 04, Issue 09
Pages : 718-721
Abstract
Data mining techniques are applied to predict school failure and idler of the Students. That use real data on school students for prediction of failure and dropout. It implements white-box classification methods, like induction rule and decision tree. DT is a decision support tool that represented as like graph or a model of decision. It consists of nodes, in which the internal nodes are denoted as test on attributes. Attribute is nothing but real data of students that collected from school in middle or secondary education. A path from root to leaf is represents classification rule and it consists of three types of nodes which includes chance node, decision node, and end node. It is mostly used in decision analysis. Using this technique to attempt to improve their correctness for predicting which students might dropout or fail by first, using all the available attributes next, and selecting the best attribute. Attribute selection done by using WEKA tool. WEKA is a Data Mining tool widely used in classification and prediction of data. WEKA tool supports several standard data mining tasks like clustering, classification, data pre-processing and feature selection. Data is rebalanced using cost responsive classification that is Naive Bayes Algorithm. The naive Bayes classifier is works based on Bayes rule of conditional probability and it accepts all attributes are contained in dataset, it takes some sample for making classification. The outcome was compared and the models with the results are exposed.
Keywords
KDD, Classification, Prediction, NavieBaye, J48.
Cite this article
V. Vivekanandan, N. Karpagavalli, R. Manoj Kumar, A. Devipriya, “Creating Data Backbones for Student Behaviour Analysis using Decision Support System” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp.718-721, September 05, 2017.
Copyright
© 2017 V. Vivekanandan, N. Karpagavalli, R. Manoj Kumar, A. Devipriya. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.