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Competence maps using agglomerative hierarchical clustering

Ahmad Barirani, Bruno Agard and Catherine Beaudry

Article (2013)

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Cite this document: Barirani, A., Agard, B. & Beaudry, C. (2013). Competence maps using agglomerative hierarchical clustering. Journal of Intelligent Manufacturing, 24(2), p. 373-384. doi:10.1007/s10845-011-0600-y
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Knowledge management from a strategic planning point of view often requires having an accurate understanding of a firm’s or a nation’s competences in a given technological discipline. Knowledge maps have been used for the purpose of discovering the location, ownership and value of intellectual assets. The purpose of this article is to develop a new method for assessing national and firm-level competences in a given technological discipline. To achieve this goal, we draw a competence map by applying agglomerative hierarchical clustering on a sample of patents. Considering the top levels of the resulting dendrogram, each cluster represents one of the technological branches of nanotechnology and its children branches are those that are most technologically proximate. We also assign a label to each branch by extracting the most relevant words found in each of them. From the information about patents inventors’ cities, we are able to identify where the largest invention communities are located. Finally, we use information regarding patent assignees and identify the most productive firms. We apply our method to the case of the emerging and multidisciplinary Canadian nanotechnology industry.

Uncontrolled Keywords

Knowledge mapping, Innovation, Citation networks analysis, Data mining, Agglomerative hierarchical clustering, Vector space model, Nanotechnology

Open Access document in PolyPublie
Subjects: 1600 Génie industriel > 1600 Génie industriel
Department: Département de mathématiques et de génie industriel
Research Center: Non applicable
Date Deposited: 03 Oct 2016 11:49
Last Modified: 08 Apr 2021 10:43
PolyPublie URL: https://publications.polymtl.ca/2312/
Document issued by the official publisher
Journal Title: Journal of Intelligent Manufacturing (vol. 24, no. 2)
Publisher: Springer
Official URL: https://doi.org/10.1007/s10845-011-0600-y


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