scholarly journals Analysis of Student's Data using Rapid Miner

2016 ◽  
Vol 4 (2) ◽  
pp. 109-117
Author(s):  
Sheena Angra ◽  
Sachin Ahuja
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Data Analysis ◽  
Educational Data Mining ◽  
Research Area ◽  
Quality Of Education ◽  
Improve Quality ◽  
Data Mining Techniques ◽  
Interesting Learning

Data mining offers a new advance to data analysis using techniques based on machine learning, together with the conventional methods collectively known as educational data mining (EDM). Educational Data Mining has turned up as an interesting and useful research area for finding methods to improve quality of education and to identify various patterns in educational settings. It is useful in extracting information of students, teachers, courses, administrators from educational institutes such as schools/ colleges/universities and helps to suggest interesting learning experiences to various stakeholders. This paper focuses on the applications of data mining in the field of education and implementation of three widely used data mining techniques using Rapid Miner on the data collected through a survey.

A Systematic Review About Prediction of Academic Behavior Through Data Mining Techniques

2020 ◽  
Vol 17 (11) ◽  
pp. 5162-5166
Author(s):  
Puninder Kaur ◽  
Amandeep Kaur ◽  
Rajwinder Kaur
Keyword(s):  
Systematic Review ◽  
Data Mining ◽  
Data Analysis ◽  
Educational Data Mining ◽  
Prediction Methods ◽  
Time Data ◽  
Academic Behavior ◽  
Data Mining Techniques ◽  
Real Time Data ◽  
Extract Information

In the IT world, predicting the academic performance of the huge student population poses a big challenge. Educational data mining techniques significantly contribute in providing solution to this problem. There are several prediction methods available for data classification and clustering, to extract information and provide accurate results. In this paper, different prediction methodologies are highlighted for the prediction of real-time data analysis of dynamic academic behavior of the students. The main focus is to provide brief knowledge about all data mining techniques and highlight dissimilarities among various methods in order to provide the best results for the students.

scholarly journals Student Dropout Prediction & Educational Data Mining

2019 ◽  
Vol 9 (2) ◽  
pp. 5190-5192
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Social Factors ◽  
Educational Data Mining ◽  
Quality Of Education ◽  
Educational Organization ◽  
Student Dropout ◽  
Underperforming Students ◽  
The Subject

Educational data like students performance is very important to study and analyze and to improve the quality of education. The study concerned to data mining techniques with educational data is known as Educational Data Mining (EDM). This study finds knowledge and interesting patterns in educational organization. Students performance are the subject mainly concerned to find the qualitative model based on student’s personal and social factors then classify and predict the student performance. Proper counseling to underperforming students can reduce dropout ratio and help them to continue their studies.

Fuzzy Information and Data Analysis

2011 ◽  
pp. 519-522
Author(s):  
Reinhard Viertl
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Warehousing ◽  
Fuzzy Information ◽  
Quality Of Data ◽  
Data Mining Techniques ◽  
Continuous Quantity

The results of data warehousing and data mining are depending essentially on the quality of data. Usually data are assumed to be numbers or vectors, but this is often not realistic. Especially the result of a measurement of a continuous quantity is always not a precise number, but more or less non-precise. This kind of uncertainty is also called fuzziness and should not be confused with errors. Data mining techniques have to take care of fuzziness in order to avoid unrealistic results.

scholarly journals Improving the Quality of Education in Higher Schools by Developing the Students Capabilities using Data Mining Techniques

2018 ◽  
Vol 1 (1) ◽  
pp. 35-43
Author(s):  
Ahmed Ashraf ◽  
Hazem El-Bakry ◽  
Yehia Elmashad ◽  
Samir Abd-Elrazik ◽  
Mohammed El-Desouky
Keyword(s):  
Data Mining ◽  
Quality Of Education ◽  
Data Mining Techniques ◽  
Using Data

Machine learning and data mining techniques for medical complex data analysis

2018 ◽  
Vol 276 ◽  
pp. 1 ◽  
Author(s):  
Hamid Alinejad-Rokny ◽  
Esmaeil Sadroddiny ◽  
Vinod Scaria
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Data Analysis ◽  
Complex Data ◽  
Data Mining Techniques

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

MODERN PROBLEMS OF VOCATIONAL TRAINING: REGIONAL REVIEW AND ADMINISTRATIVE SOLUTIONS

2019 ◽  
pp. 264-267
Author(s):  
M.V. Korotkova ◽  
D.A. Korshunov
Keyword(s):  
Vocational Training ◽  
Quality Of Education ◽  
Improve Quality ◽  
Regional Review

In the article the problems of vocational training and possibility of their solution in order to improve quality of education, provide the possibility of advancing growth of vocational training and to develop the region economy are considered.

scholarly journals Data mining techniques with machine learning algorithm to predict patients of heart disease

2021 ◽  
Vol 1088 (1) ◽  
pp. 012035
Author(s):  
Mulyawan ◽  
Agus Bahtiar ◽  
Githera Dwilestari ◽  
Fadhil Muhammad Basysyar ◽  
Nana Suarna
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Mining Techniques

Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

2021 ◽  
pp. 097215092098485
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Meta Analysis ◽  
Financial Statement ◽  
Research Articles ◽  
Financial Statement Fraud ◽  
Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

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