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

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.

Keywords

• innovation
• clustering
• k-means
• hierarchical clustering