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논문검색

Similarity Measure Design on High Dimensional Data

원문정보

THEERA-UMPON Nipon, Sanghyuk Lee

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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

저자정보

  • 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

참고문헌

자료제공 : 네이버학술정보

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