Impact of Data Mining Technique in Education Institutions

Major decision making process using large amount of data can be done by various techniques using data mining. In education sectors various data mining techniques are implemented to analyze the student’s data from the admission process itself. Due to large number of educational institution in India, excellence becomes a major parameter for the institutions to grow and with stand. Nowadays education institutions use data mining techniques to show their excellence. The main objective of this work to present an analysis of individual semester wise results of engineering college students using different techniques of data mining. Here we used different classification algorithms like decision tree, rule based, function based and Bayesian algorithms to analyze the semester results and comparison is made by considering parameters like accuracy and error rate. Our output shows the most suited algorithm for analyzing data in educational institutions.

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Efficient Data Mining Techniques to Improve Academic Performance of Students

SAMRIDDHI A Journal of Physical Sciences Engineering and Technology ◽

10.18090/samriddhi.v13is1.18 ◽

2021 ◽

Vol 13 (SUP 1) ◽

pp. 81-84

Author(s):

Rashmi V. Varade ◽

Blessy Thankanchan

Keyword(s):

Data Mining ◽

Academic Performance ◽

Large Volume ◽

Educational Institutions ◽

Data Mining Technique ◽

Data Mining Techniques ◽

Mining Technique ◽

Efficient Data

Predicting the academic performance of students is very challenging due to large volume of data in the educational institutions database. Data mining techniques are implemented to predict students' academic performance in many institutions. Because of predicting students' performance, it will help teachers and institutions to decide strategies to teach to the students who are weak in studies and also they can define different strategies who are good in studies so that these students can perform better, So, aim of this paper is to study such a data mining technique which will help us to predict students' academic performance in advance.

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Potable Water Order Forecasting System Using Data Mining Technique

International Journal of Emerging Technology and Advanced Engineering ◽

10.46338/ijetae0621_01 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1-8

Author(s):

Phatarapon Vorapracha ◽

Keyword(s):

Data Mining ◽

Drinking Water ◽

Potable Water ◽

Bayesian Classification ◽

Data Mining Technique ◽

Data Mining Techniques ◽

Mining Technique ◽

C4.5 Algorithm ◽

Forecasting System ◽

Using Data

Potable water order forecasting system using data mining technique. It aims to analyze, design and develop potable water order forecasting system using data mining technique. There is a comparison data mining techniques were compared using the C4.5 algorithm and Bayesian classification algorithm. The researcher found that the C4.5 algorithm is more suitable for drinking water ordering system. This web application system allows the system to predict each customer's drinking water orders. Subscription support ordering, drinking water and bank payment. In terms of user interaction and use the MySQL database program to organize the system database. The result of development potable water order forecasting system using data mining technique. Have tested data mining techniques were compared using the C4.5 algorithm and Bayesian classification algorithm. The researcher found that the C4.5 algorithm is more suitable for drinking water ordering system. From data research results using data in 9 months of training and 2 months of testing, it was found that the accuracy was 85.59%. C4.5 algorithm and test the system from the evaluation of 2 administrators, 3 employees and 5 customers, total 10 people with average mean of 4.20 .

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Teachers’ Performance Evaluation in Higher Educational Institution using Data Mining Technique

International Journal of Applied Information Systems ◽

10.5120/ijais2016451524 ◽

2016 ◽

Vol 10 (7) ◽

pp. 10-15 ◽

Cited By ~ 4

Author(s):

Asanbe M.O. ◽

Osofisan A.O. ◽

William W.F.

Keyword(s):

Data Mining ◽

Performance Evaluation ◽

Educational Institution ◽

Data Mining Technique ◽

Mining Technique ◽

Higher Educational ◽

Using Data

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An Application Of A Data Mining Technique For Assessing The Risk Of Management Fraud

Review of Business Information Systems (RBIS) ◽

10.19030/rbis.v2i1.5446 ◽

1998 ◽

Vol 2 (1) ◽

pp. 1-16

Author(s):

Ashutosh Deshmukh ◽

Lakshminarayan Talluru

Keyword(s):

Data Mining ◽

Rapid Development ◽

Data Mining Technique ◽

Data Mining Techniques ◽

Management Fraud ◽

Data Mining Tool ◽

Mining Technique ◽

Mining Tool ◽

Using Data ◽

Big Six

Data mining techniques identify relationships, patterns, trends, and predictive information form large and complex databases. This study demonstrates the use of a data mining technique to assess the risk of management fraud. We use a data mining tool to analyze the management fraud data, presence or absence of red flags in fraud and no fraud cases, collected by a Big Six firm. The ensuing results compare favorably with the statistical and neural network results obtained by the other studies. The study illustrates the ease of using data mining techniques by demonstrating the rapid development of models, querying capabilities, and ease of encoding statistical models in audit decision making.

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