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A Comparison of Patent Classifications with Clustering Analysis

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Web Information Systems Engineering – WISE 2015 (WISE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9419))

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Abstract

There is an abundance of data and knowledge within any given patent. Through the use of textual mining and machine learning clustering techniques it is possible to discover meaningful associations throughout a corpus of patents. This research demonstrates that such relationships between USPTO patents exist. Through the use of k-means and k-medians clustering, the accuracy of the USPTO classes will be assessed. It will also be demonstrated that a more refined classification process would be beneficial to other areas of analysis and forecasting.

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Author information

Authors and Affiliations

  1. School of Technology, North Carolina A&T State University, 1601 E. Market Street, Greensboro, NC, 27411, USA

    Mick Smith

  2. Department of Computer Systems Technology, North Carolina A&T State University, 209 Price Hall, Greensboro, NC, 27411, USA

    Rajeev Agrawal

Authors
  1. Mick Smith
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  2. Rajeev Agrawal
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Corresponding author

Correspondence to Mick Smith .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jianyong Wang

  2. Poznan University of Economics, Poznan, Poland

    Wojciech Cellary

  3. Florida Atlantic University, Boca Raton, Florida, USA

    Dingding Wang

  4. Victoria University, Melbourne, Victoria, Australia

    Hua Wang

  5. Florida International University, Miami, Florida, Florida, USA

    Shu-Ching Chen

  6. Florida International University, Miami, Florida, USA

    Tao Li

  7. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

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© 2015 Springer International Publishing Switzerland

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Smith, M., Agrawal, R. (2015). A Comparison of Patent Classifications with Clustering Analysis. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9419. Springer, Cham. https://doi.org/10.1007/978-3-319-26187-4_38

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  • DOI: https://doi.org/10.1007/978-3-319-26187-4_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26186-7

  • Online ISBN: 978-3-319-26187-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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