Earticle

초록

영어

Designing of similarity on high dimensional data was done. Similarity measure between high dimensional data was considered by analysing neighbor information with respect to data sets. Obtained result could be applied to big data, because big data has multiple characteristics compared to simple data set. Definitely, analysis of high dimensional data could be the pre-study of big data. High dimensional data analysis was also compared with the conventional similarity. Traditional similarity measure on overlapped data was illustrated, and application to non-overlapped data was carried out. Its usefulness was proved by way of mathematical proof, and verified by calculation of similarity for artificial data example.

목차

Abstract
 1. INTRODUCTION
 2. Similarity Measure for Data
 3. High-Dimensional Similarity
  3.1 Similarity Measure on high-dimension data
  3.2 Illustrative Example
 4. Conclusions
 References

키워드

저자

• THEERA-UMPON Nipon [ Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200 Thailand ]
• Sanghyuk Lee [ Department of Electrical and Electronics Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China ] 교신저자

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국융합학회논문지 [Journal of the Korea Convergence Society]

• 간기

  월간

• pISSN

  2233-4890

• 수록기간

  2010~2022

• 십진분류

  KDC 530 DDC 620