Clustering techniques and innovation-based comparison in Londrina and Region companies

Clustering techniques and innovation-based comparison in Londrina and Region companies

Authors

DOI:

https://doi.org/10.5433/1679-0375.2024.v45.49522

Keywords:

innovation, clustering, k-means, hierarchical clustering

Abstract

Innovation is often considered a cornerstone for success across various companies. However, research focused on measuring and describing innovation frequently relies on classical statistical techniques. In this context, this study applied unsupervised machine learning techniques to cluster companies in the Londrina region, investigating how variables related to innovation differ among the identified clusters. Data were collected through a survey instrument adapted from CIS 4 and PINTEC, encompassing 26 responding companies, although 23 were analyzed in this study. Four clustering algorithms were employed: k-means, k-means+PCA, hierarchical, and hierarchical+PCA. Regarding the results, the hierarchical+PCA algorithm showed improved separation between service and manufacturing companies. Clusters identified with the value ``"1" indicated concerns related to regular investment in R&D, collaborations for innovation, and requests/registrations of patents in the last three years. Analyzing demographic characteristics revealed that clusters identified by hierarchical+PCA exhibited a higher presence of service sector companies, while cluster 1 showed a prevalence of industries, suggesting that these possess more innovative characteristics in the Londrina region.

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Author Biographies

Ana Paula Barbosa de Morais, Universidade Tecnológica Federal do Paraná - Câmpus Londrina

Bachelor’s in Production Engineering, UTFPR, Londrina, PR, Brazil; anamorais@alunos.utfpr.edu.br

Matheus Santos Dias, Universidade Tecnológica Federal do Paraná - Câmpus Londrina

Production Engineering student, UTFPR, Londrina, PR, Brazil; matheusdias.1995@alunos.utfpr.edu.br

Bruno Samways dos Santos, Universidade Tecnológica Federal do Paraná - Campus Londrina

Dr. Prof., Dept. Production Engineering, UTFPR, Londrina, PR, Brazil; brunosantos@utfpr.edu.br

Rafael Henrique Palma Lima, Universidade Tecnológica Federal do Paraná - Câmpus Londrina

Dr. Prof., Dept. Production Engineering, UTFPR, Londrina, PR, Brazil; rafaelhlima@utfpr.edu.br

Pedro Rochavetz de Lara Andrade, Universidade Tecnológica Federal do Paraná - Câmpus Londrina

Dr. Prof., Dept. Production Engineering, UTFPR, Londrina, PR, Brazil; pedroandrade@utfpr.edu.br

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Published

2024-05-06

How to Cite

Paula Barbosa de Morais, A., Santos Dias, M., Samways dos Santos, B., Henrique Palma Lima, R., & Rochavetz de Lara Andrade, P. (2024). Clustering techniques and innovation-based comparison in Londrina and Region companies. Semina: Ciências Exatas E Tecnológicas, 45, e49522. https://doi.org/10.5433/1679-0375.2024.v45.49522

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