Earticle

초록

영어

Data mining technology is applied to various fields because it is a technique for analyzing vast amount of data and finding useful information. In this paper, we propose a big data analysis method that uses Apriori algorithm, which is a data mining technique, to find the related factors that have negative and positive influences on school adjustment. Among Korea Child and Youth Panel Survey(KCYPS), data related to adjustment to school life and data showing parental inclinations were extracted from the data of fourth grade elementary school students, first year middle school students, and high school freshman students, respectively and we have mapped the useful association rules among them. As a result, the factors affecting school adjustment were different according to the timing of the growth process, we were able to find interesting rules by looking for connections between rules. On the other hand, the factors that positively influenced school adjustment were not significantly different from each other, and overall, they were associated with positive variables.

목차

Abstract
1. Introduction
2. Apriori algorithm for Association Rule Mining
2.1 Apriori Algorithm
2.2 Support and Confidence
2.3 Threshold of Support and Confidence
3. Analyzing Influencing Factor of School Adjustment
3.1 Data Collection and Refinement
3.2 Analysis of Related Factors Negatively Affecting School Adjustment
3.3 Analysis of Affecting Factors Affecting School Adjustment Positively
4. Performance Evaluation
4.1 Evaluation Data
4.2 Evaluation Measure
4.3 Performance Evaluation Result
5. Conclusion
References

키워드

저자

• Sujeong Ko [ Dept. of Computer Software, Induk University, Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380