A two-step agglomerative hierarchical clustering method for patent time-dependent data

Chen, H; Zhang, G; Lu, J; Zhu, D

A two-step agglomerative hierarchical clustering method for patent time-dependent data

Chen, H

Zhang, G

Lu, J

Zhu, D

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Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2014, 213 pp. 111 - 121
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Chen, H https://orcid.org/0000-0002-0893-1817	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhu, D	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2014, 213 pp. 111 - 121	en_US
dc.identifier.isbn	9783642378287	en_US
dc.identifier.issn	2194-5357	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32780
dc.description.abstract	© Springer-Verlag Berlin Heidelberg 2014. 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 timerelated 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.	en_US
dc.relation.ispartof	Advances in Intelligent Systems and Computing	en_US
dc.relation.isbasedon	10.1007/978-3-642-37829-4_10	en_US
dc.title	A two-step agglomerative hierarchical clustering method for patent time-dependent data	en_US
dc.type	Conference Proceeding
utslib.citation.volume	213	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Beijing, China
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	213	en_US

Abstract:

© Springer-Verlag Berlin Heidelberg 2014. 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 timerelated 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.

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http://hdl.handle.net/10453/32780