A Case Study on Student Attrition Prediction in Higher Education Using Data Mining Techniques

2019 ◽  
pp. 181-192
Author(s):  
Syaidatus Syahira Ahmad Tarmizi ◽  
Sofianita Mutalib ◽  
Nurzeatul Hamimah Abdul Hamid ◽  
Shuzlina Abdul-Rahman ◽  
Ariff Md Ab Malik
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Student Attrition ◽  
Data Mining Techniques ◽  
Using Data

Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Psychiatric Patients ◽  
Human Life ◽  
A Priori ◽  
Psychological Research ◽  
Attribute Selection ◽  
Specific Class ◽  
Data Mining Techniques ◽  
Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

scholarly journals Customers Grouping Using Data Mining Techniques in the Food Distribution Industry (A Case Study)

2020 ◽  
Keyword(s):  
Data Mining ◽  
Customer Segmentation ◽  
Food Distribution ◽  
Data Mining Techniques ◽  
Customer Lifetime ◽  
Rfm Model ◽  
Marketing Activities ◽  
Using Data ◽  
Production Company

Significant data development has required organizations to use a tool to understand the relationships between data and make various appropriate decisions based on the information obtained. Customer segmentation and analysis of their behavior in the manufacturing and distribution industries according to the purposefulness of marketing activities and effective communication and with customers has a particular importance. Customer segmentation using data mining techniques is mainly based on the variables of recency purchase (R), frequency of purchase (F) and monetary value of purchase (M) in RFM model. In this article, using the mentioned variables, twelve customer groups related to the BTB (business to business) of a food production company, are grouped. The grouping in this study is evaluated based on the K-means algorithm and the Davies-Bouldin index. As a result, customer grouping is divided into three groups and, finally the CLV (customer lifetime value) of each cluster is calculated, and appropriate marketing strategies for each cluster have been proposed.

Detection of Desertion Patterns in University Students Using Data Mining Techniques: A Case Study

2018 ◽  
pp. 420-429
Author(s):  
Dayana Vila ◽  
Saúl Cisneros ◽  
Pedro Granda ◽  
Cosme Ortega ◽  
Miguel Posso-Yépez ◽  
...  
Keyword(s):  
Data Mining ◽  
University Students ◽  
Data Mining Techniques ◽  
Using Data

Provide a method for validation of bank customers using data mining techniques (Case study: Bank Sepah)

2018 ◽  
Vol 05 (02) ◽  
pp. 145-153
Author(s):  
Fatemeh Sadeghi Laghareh ◽  
Seyed Javad Mirabedini ◽  
Ali Haroon Abadi
Keyword(s):  
Data Mining ◽  
Data Mining Techniques ◽  
Using Data
Sign in / Sign up

Export Citation Format

Share Document