Technology matching of the patent documents using clustering algorithms

Miroslava Drazic, Dragan Kukolj, Milana Vitas, Maja Pokric, Sanja Manojlovic, Zeljko Tekic

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

This paper analyzes the accuracy of different clustering algorithms to handle different parts of the patent documents. The algorithms are part of the software package which is used as a tool for business intelligence purposes. The tool assembles patent data from publicly available data bases, collects and analyzes patents bibliographic parameters and performs text mining. Performances of clustering algorithms: k-means, the neural-gas; fuzzy c-means and ronn algorithm are examined when run on different parts of the patent document, such as abstract, claim, international patent code description and detailed patent description, but applied on the same patent data set. Patent data set was previously classified in technology groups by the experts and obtained results are compared with the purpose of selection of the most suitable algorithm and patent document part.

Original languageEnglish
Title of host publicationCINTI 2013 - 14th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings
Pages405-409
Number of pages5
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event14th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2013 - Budapest, Hungary
Duration: 19 Nov 201321 Nov 2013

Publication series

NameCINTI 2013 - 14th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings

Conference

Conference14th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2013
Country/TerritoryHungary
CityBudapest
Period19/11/1321/11/13

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