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

  • Hongshu Chen
  • Guangquan Zhang
  • Jie Lu
  • Donghua Zhu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

Patent analysis Technology clustering Patent time-dependent data Agglomerative hierarchical clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Hongshu Chen
    • 1
    • 2
  • Guangquan Zhang
    • 1
  • Jie Lu
    • 1
  • Donghua Zhu
    • 2
  1. 1.Decision Systems and e-Service Intelligence Lab, Centre for Quantum Computation and Intelligent SystemsFaculty of Engineering and Information Technology, University of TechnologySydneyAustralia
  2. 2.School of Management and EconomicsBeijing Institute of TechnologyBeijingChina

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