Skip to main content
SpringerLink
Log in

A Two-Step Agglomerative Hierarchical Clustering Method for Patent Time-Dependent Data

  • Conference paper
  • First Online:
Foundations and Applications of Intelligent Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 213))

Abstract

Patent data have time-dependent property and also semantic attributes. Technology clustering based on patent time-dependent data processed by trend analysis has been used to help technology relationship identification. However, the raw patent data carry more features than processed data. This paper aims to develop a new methodology to cluster patent frequency data based on its time-related properties. To handle time-dependent attributes of patent data, this study first compares it with typical time series data to propose preferable similarity measurement approach. It then presents a two-step agglomerative hierarchical technology clustering method to cluster original patent time-dependent data directly. Finally, a case study using communication-related patents is given to illustrate the clustering method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Richard SC (1983) Patent trends as a technological forecasting tool. World Pat Inf 5(3):137–143

    Google Scholar 

  2. Cozzens S et al (2010) Emerging technologies: quantitative identification and measurement. Technol Anal Strateg Manag 22(3):361–376

    Google Scholar 

  3. Bengisu M, Nekhili R (2006) Forecasting emerging technologies with the aid of science and technology databases. Technol Forecast Soc Chang 73(7):835–844

    Google Scholar 

  4. Robinson DKR et al (2013) Forecasting innovation pathways (FIP) for new and emerging science and technologies. Technol Forecast and Soc Chang 80(2):267–285

    Google Scholar 

  5. Chen Y-L, Chang Y-C (2012) A three-phase method for patent classification. Inf Process & Manag 48(6):1017−1030

    Google Scholar 

  6. Yoon J, Kim K (2012) TrendPerceptor: a property-function based technology intelligence system for identifying technology trends from patents. Expert Syst Appl 39(3):2927–2938

    Google Scholar 

  7. Lee H, Lee S, Yoon B (2011) Technology clustering based on evolutionary patterns: the case of information and communications technologies. Technol Forecast Soc Chang 78(6):953–967

    Google Scholar 

  8. Trappey CV et al (2011) Using patent data for technology forecasting: China RFID patent analysis. Adv Eng Inform 25(1):53–64

    Google Scholar 

  9. Lee S, Lee H, Yoon B (2012) Modeling and analyzing technology innovation in the energy sector: patent-based HMM approach. Comput & Ind Eng 63(3):564–577

    Google Scholar 

  10. Dereli T et al (2011) Enhancing technology clustering through heuristics by using patent counts. Expert Syst Appl 38(12):15383–15391

    Article  Google Scholar 

  11. Berndt D, Clifford J (1994) Using dynamic time warping to find patterns in time series. In: Workshop on knowledge discovery in databases KDD-94 proceedings, Seattle

    Google Scholar 

  12. Warren Liao T (2005) Clustering of time series data—a survey. Pattern Recognit 38(11):1857–1874

    Google Scholar 

  13. Keogh E, Pazzani M (1998) An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In: Workshop on knowledge discovery in databases KDD-98 proceedings

    Google Scholar 

  14. Ward JH (1993) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244

    Google Scholar 

  15. Maimon O, Rokach L (2010) Data mining and knowledge discovery handbook, vol 1. Springer Science + Business Media, LLC

    Book  MATH  Google Scholar 

  16. Goldin D, Kanellakis P (1995) On similarity queries for time-series data: constraint specification and implementation. In: The 1st international conference on the principles and practice of constraint programming, Springer, Cassis

    Google Scholar 

  17. Keogh E, Kasetty S (2003) On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Min Knowl Disc 7(4):349–371

    Google Scholar 

  18. United States Patent and Trademark Office, Classes within the U.S. Classification System (2012). http://www.uspto.gov/patents/resources/classification/classescombined.pdf

Download references

Author information

Authors and Affiliations

  1. Decision Systems and e-Service Intelligence Lab, Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia

    Hongshu Chen, Guangquan Zhang & Jie Lu

  2. School of Management and Economics, Beijing Institute of Technology, Beijing, China

    Hongshu Chen & Donghua Zhu

Authors
  1. Hongshu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guangquan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jie Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Donghua Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongshu Chen .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, People's Republic of China

    Fuchun Sun

  2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, People's Republic of China

    Tianrui Li

  3. Department of Computer Science and Techn, Tsinghua University, Beijing, People's Republic of China

    Hongbo Li

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chen, H., Zhang, G., Lu, J., Zhu, D. (2014). A Two-Step Agglomerative Hierarchical Clustering Method for Patent Time-Dependent Data. In: Sun, F., Li, T., Li, H. (eds) Foundations and Applications of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37829-4_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37829-4_10

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37828-7

  • Online ISBN: 978-3-642-37829-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation