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Similarity Measure Design on Big Data

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Book cover Future Information Communication Technology and Applications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 235))

Abstract

Clustering algorithm in big data was designed, and its idea was based on defining similarity measure. Traditional similarity measure on overlapped data was illustrated, and application to non-overlapped data was carried out. Similarity measure on high dimension data was obtained through getting information from neighbor data. Its usefulness was proved, and verified by calculation of similarity for artificial data example.

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China

    Sanghyuk Lee

  2. School of Business Economic and Management, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China

    Yan Sun

Authors
  1. Sanghyuk Lee
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  2. Yan Sun
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Corresponding author

Correspondence to Sanghyuk Lee .

Editor information

Editors and Affiliations

  1. , Computer Engineering, Paichai University, Baejae-ro, Seo-gu 155-40, Daejeon, 302-735, Korea, Republic of (South Korea)

    Hoe-Kyung Jung

  2. , Electronic Engineering, Mokwon University, Doanbookro, Seo-Ku 88, Daejeon, 302-729, Korea, Republic of (South Korea)

    Jung Tae Kim

  3. PO Box 2434, Electrical Engineering, QUT Gardens Point, George Street 2, Brisbane, 4001, Queensland, Australia

    Tony Sahama

  4. Normal University, Graduate Institute of Information, National Kaohsiung, No.116, Heping 1st Rd., Lingya District, Kaohsiung City, 80201, Kao-hsiung, Taiwan R.O.C.

    Chung-Huang Yang

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© 2013 Springer Science+Business Media Dordrecht

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Lee, S., Sun, Y. (2013). Similarity Measure Design on Big Data. In: Jung, HK., Kim, J., Sahama, T., Yang, CH. (eds) Future Information Communication Technology and Applications. Lecture Notes in Electrical Engineering, vol 235. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6516-0_90

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  • DOI: https://doi.org/10.1007/978-94-007-6516-0_90

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-6515-3

  • Online ISBN: 978-94-007-6516-0

  • eBook Packages: EngineeringEngineering (R0)

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