Finding Relationship among Various Students’ Attributes with Sport-Interest using Association Rule Mining Method

Authors

  • Sanjeev Gour Department of Computer Science, Career College, Bhopal, Madhya Pradesh, India

DOI:

https://doi.org/10.51983/ajcst-2018.7.3.1893

Keywords:

Association Rule Mining, Educational Data Mining, Apriori Algorithm, Weka, XLMiner

Abstract

Data mining methods are widely used in educational domain for the purpose of finding useful information from the large student’s database. This information is then used to understand the behaviors of students in respect of their academic and other curricular performance. One of such Data mining methods called Association rule mining is used in this research study to analyze the student’s database of Career College Bhopal using two mining tools called Weka and XLMiner. The database contain records of 212 students with main attributes like Student’s Gender, Category, Subject, name of district where he/she belong and their parent’s/guardian’s occupation/profession and sport-interest. Sport department of any educational institute also need to understand the behavior and psychology of students for their sport interest to make sport-policy for their institute. In this paper, author has found some unknown relationship among these attributes with respect to sport-interest which is a target attribute. This experimental study has generated many association rules that can be used to answer the questions like which student from particular course, district and category will participate in sports? Which sport usually prefer by male and female student most? Which student can be performed better in which sport? In this way sport policy maker can use these mined information about the sport interest of students to make better decisions in sport framework in an educational institute.

References

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Published

05-11-2018

How to Cite

Gour, S. (2018). Finding Relationship among Various Students’ Attributes with Sport-Interest using Association Rule Mining Method. Asian Journal of Computer Science and Technology, 7(3), 72–75. https://doi.org/10.51983/ajcst-2018.7.3.1893