Earticle

초록

영어

The fast-developing Chinese higher education and research institutions are faced with more and more serious challenges in their financial regulation. Traditional accounting approaches may fail to distill useful decision-making suggestions confronted with the multi-species and huge-quantity financial “big data”. To reveal valuable information, this research focuses on the pattern recognition of the funding flows in 76 universities under Chinese Ministry of Education. Given the trend feature of the data series detected by the Mann-Kendall Non-Parameter Ranking Test, the low-frequency parts of the funding flows are distilled with wavelet transform to represent their basic features. Results show a clear hierarchy structure in the investments and expenses of universities according to their “titles” and advantage disciplines. Specifically, the comprehensive universities fall to different categories according to their “titles”, while the professional universities are classified according to their disciplines. The scientific and technical focused universities show larger variance among different categories than other specific discipline focused universities.

목차

Abstract
 1. Introduction
 2. Methodology
  2.1. Trend Test
  2.2. Data Feature Extraction
  2.3. Similarity Measurement
  2.3. Clustering
  2.4 Evaluation Indicators
 3. Experiment
  3.1 Data Description
  3.2 Data Trend Test
  3.3 Clustering Based on Data Features Extraction
 4. Results
  4.1. Clustering Results
  4.2. Evaluation Results
 5. Discussion
 6. Conclusion
 Acknowledgment
 References

키워드

저자

• Yixuan Ma [ Economics and Management, Beijing Jiaotong University Beijing 100044, China ]
• Zhenji Zhang [ Economics and Management, Beijing Jiaotong University Beijing 100044, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605