Identifying Catalyst Technologies in Clusters with Unsupervised Machine Learning. An application on patent clusters in the UK

Zehra Usta, Martin Andersson, Katarzyna Kopczewska and Maria Kubara

No 2528, Papers in Evolutionary Economic Geography (PEEG) from Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography

Abstract: A common proposition is that certain technologies play a catalytic role in regions by paving the way for the emergence of new related technologies, contributing to the development and diversification of technology clusters. This paper employs unsupervised machine learning algorithms with temporally informed association rule mining to identify catalytic patents in clusters in the UK. Using data spanning over 30 years (1980-2015) we show clear asymmetric relationships between patents. Some act as evident catalysts that drive future patent activity in clusters. The results point to a strong empirical relevance of asymmetric relatedness between patents in the development of clusters of technology. They also highlight the usefulness of machine learning algorithms to better understand the long-term evolution of clusters and show how temporally informed association rule mining can be used to analyses asymmetries in relatedness and to identify catalyst technologies.

Keywords: clusters; innovation; cluster dynamics; technological relatedness; asymmetric relatedness; innovation catalysts; patents (search for similar items in EconPapers)
JEL-codes: O31 O33 R12 (search for similar items in EconPapers)
Date: 2025-08, Revised 2025-08
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cse, nep-geo, nep-his, nep-sbm and nep-tid
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