bipartite network

Zeng, Siping, Wang, Ting, Lin, Wenguang, Chen, Zhizhen (Ryan) ORCID: 0000-0001-6656-5854 and Xiao, Renbin (2024) A patent mining approach to accurately identifying Innovative Industrial Clusters based on the multivariate DBSCAN algorithm. Systems, 12:321. pp. 1-28. ISSN 2079-8954 (Online) (doi:https://doi.org/10.3390/systems12090321)

density‑based spatial clustering

Zeng, Siping, Wang, Ting, Lin, Wenguang, Chen, Zhizhen (Ryan) ORCID: 0000-0001-6656-5854 and Xiao, Renbin (2024) A patent mining approach to accurately identifying Innovative Industrial Clusters based on the multivariate DBSCAN algorithm. Systems, 12:321. pp. 1-28. ISSN 2079-8954 (Online) (doi:https://doi.org/10.3390/systems12090321)

innovative industrial clusters

Zeng, Siping, Wang, Ting, Lin, Wenguang, Chen, Zhizhen (Ryan) ORCID: 0000-0001-6656-5854 and Xiao, Renbin (2024) A patent mining approach to accurately identifying Innovative Industrial Clusters based on the multivariate DBSCAN algorithm. Systems, 12:321. pp. 1-28. ISSN 2079-8954 (Online) (doi:https://doi.org/10.3390/systems12090321)

latent Dirichlet allocation

Zeng, Siping, Wang, Ting, Lin, Wenguang, Chen, Zhizhen (Ryan) ORCID: 0000-0001-6656-5854 and Xiao, Renbin (2024) A patent mining approach to accurately identifying Innovative Industrial Clusters based on the multivariate DBSCAN algorithm. Systems, 12:321. pp. 1-28. ISSN 2079-8954 (Online) (doi:https://doi.org/10.3390/systems12090321)

patent analysis

Zeng, Siping, Wang, Ting, Lin, Wenguang, Chen, Zhizhen (Ryan) ORCID: 0000-0001-6656-5854 and Xiao, Renbin (2024) A patent mining approach to accurately identifying Innovative Industrial Clusters based on the multivariate DBSCAN algorithm. Systems, 12:321. pp. 1-28. ISSN 2079-8954 (Online) (doi:https://doi.org/10.3390/systems12090321)