Application of Hierarchical Agglomerative Clustering for the Segmentation of Startups in Latin America
DOI:
https://doi.org/10.61467/2007.1558.2027.v18i1.1327Keywords:
Unsupervised learning, Hierarchical Clustering, Latin American Startups, Data Mining, Aprendizaje no supervisado, agrupamiento jerárquico, startups latinoamericanas, minería de datosAbstract
This article proposes a segmentation model of Latin American Startups based on unsupervised learning techniques (hierarchical agglomerative clustering with Ward's linkage) to identify the underlying structure of data that include financial and digital variables. The applied approach allowed for the identification of common patterns and significant differences, which facilitated establishing profiles that help understand business growth and consolidation processes. The sample includes 1,274 startups active at the time of data collection, which are first analyzed through descriptive statistics, tests of association, differentiation, and correlation. Subsequently, by applying hierarchical agglomerative clustering, three groups of Latin American Startups became evident: Cluster 1-Medium-aged companies without access to investment. Cluster 2-Young companies with early access to investment. Cluster 3-More advanced companies with greater capital and visibility. These findings allow for the modeling of the transitions that occur in startups: initiation, growth, and expansion.
Spanish-language metadata / Metadatos en español
Título en español:
Aplicación del agrupamiento jerárquico aglomerativo para la segmentación de startups en América Latina
Resumen:
Este artículo propone un modelo de segmentación de startups latinoamericanas basado en técnicas de aprendizaje no supervisado —agrupamiento jerárquico aglomerativo con el método de Ward— para identificar la estructura subyacente de datos que incluyen variables financieras y digitales. El enfoque aplicado permitió identificar patrones comunes y diferencias significativas, lo que facilitó el establecimiento de perfiles que contribuyen a comprender los procesos de crecimiento y consolidación empresarial.
La muestra comprende 1.274 startups activas en el momento de la recopilación de los datos, las cuales se analizaron inicialmente mediante estadística descriptiva y pruebas de asociación, diferenciación y correlación. Posteriormente, mediante la aplicación del agrupamiento jerárquico aglomerativo, se identificaron tres grupos de startups latinoamericanas: Clúster 1: empresas de antigüedad intermedia sin acceso a inversión; Clúster 2: empresas jóvenes con acceso temprano a inversión; y Clúster 3: empresas más avanzadas, con mayor capital y visibilidad.
Estos hallazgos permiten modelar las transiciones que experimentan las startups a través de tres etapas: inicio, crecimiento y expansión.
Palabras Claves:
Aprendizaje no supervisado, agrupamiento jerárquico, startups latinoamericanas, minería de datos.
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