Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Similarity Measure Design on High Dimensional Data

  • Nipon, Theera-Umpon (Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University) ;
  • Lee, Sanghyuk (Department of Electrical and Electronics Engineering, Xi'an Jiaotong-Liverpool University)
  • Received : 2013.03.12
  • Accepted : 2013.03.22
  • Published : 2013.03.31
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Abstract

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.

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