Identifying catalyst technologies in clusters with unsupervised machine learning: an application on patent clusters in the UK

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.