Structure clustering for Chinese patent documents

Su Hsien Huang*, Hao Ren Ke, Wei Pang Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


This paper aims to cluster Chinese patent documents with the structures. Both the explicit and implicit structures are analyzed to represent by the proposed structure expression. Accordingly, an unsupervised clustering algorithm called structured self-organizing map (SOM) is adopted to cluster Chinese patent documents with both similar content and structure. Structured SOM clusters the similar content of each sub-part structure, and then propagates the similarity to upper level ones. Experimental result showed the maps size and number of patents are proportional to the computing time, which implies the width and depth of structure affects the performance of structured SOM. Structured clustering of patents is helpful in many applications. In the lawsuit of copyright, companies are easy to find claim conflict in the existent patents to contradict the accusation. Moreover, decision-maker of a company can be advised to avoid hot-spot aspects of patents, which can save a lot of R&D effort.

Original languageEnglish
Pages (from-to)2290-2297
Number of pages8
JournalExpert Systems with Applications
Issue number4
Publication statusPublished - 2008 May
Externally publishedYes


  • Chinese patent
  • Metadata
  • Structure clustering
  • Structure expression

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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