<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1659-4142</journal-id>
<journal-title><![CDATA[E-Ciencias de la Información]]></journal-title>
<abbrev-journal-title><![CDATA[E-Ciencias de la Información]]></abbrev-journal-title>
<issn>1659-4142</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Costa Rica, Escuela de Bibliotecología y Ciencias de la Información]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1659-41422022000200103</article-id>
<article-id pub-id-type="doi">10.15517/eci.v12i2.50456</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Método de algoritmo de clúster para el análisis del perfil de investigadores científicos]]></article-title>
<article-title xml:lang="en"><![CDATA[Cluster algorithm method for profile analysis of scientific researchers]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rodríguez-Bárcenas]]></surname>
<given-names><![CDATA[Gustavo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Técnica de Cotopaxi  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>12</volume>
<numero>2</numero>
<fpage>103</fpage>
<lpage>127</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S1659-41422022000200103&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S1659-41422022000200103&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S1659-41422022000200103&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El aumento de la producción científica convierte en un desafío la tarea de identificar patrones y rasgos particulares que caractericen a los investigadores. Lograr establecer niveles de compatibilidad y similaridad entre actores en un contexto de investigación científica a partir de sus perfiles requiere de un proceso rápido y apropiado. El objetivo de este artículo es evaluar los niveles de similaridad, distancia euclidiana y compatibilidad entre vectores de investigadores, a partir de algoritmos de agrupamiento, escalamiento multidimensional, principios del modelo espacio-vectorial y atributos de sus perfiles científicos, considerando las terminologías que se abordan en su producción científica. Se utilizaron métodos teóricos y empíricos, incluyendo técnicas y herramientas de minería de texto. La aplicación del procedimiento en el Centro de Estudios de la Energía y Tecnología Avanzada de Cuba (CEETAM) y la Universidad Técnica de Cotopaxi (UTC) en Ecuador, evidenció su efectividad. Como resultado se pudo identificar los profesionales con mayores niveles de coincidencia en áreas a fines y líneas de investigación, lo que propicia el establecimiento de Comunidades Colectivas de Conocimientos; se pudo demostrar que los métodos empleados pueden ser integrados a las TIC, resultando en la obtención de relaciones perceptuales entre los investigadores y expresando los grupos que se forman a partir de conglomerados de observaciones en cada subcategoría y dominios de conocimientos de los dos casos de estudio analizados.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The increase in scientific production makes it a challenge to identify particular patterns and traits that characterize researchers. Establishing levels of compatibility and similarity between actors in a scientific research context from their profiles requires a rapid and appropriate process. The objective of this article is to evaluate the levels of similarity, Euclidean distance and compatibility between vectors of researchers, based on clustering algorithms, multidimensional scaling, principles of the vector-space model and attributes of their scientific profiles, considering the terminologies addressed in their scientific production. Theoretical and empirical methods were used, including text mining techniques and tools. The application of the procedure in the Advanced Energy and Technology Study Center from Cuba and the Cotopaxi Technical University from Ecuador, evidenced its effectiveness. As a result, it was possible to identify professionals with higher levels of coincidence in areas and lines of research, which favors the establishment of Collective Communities of Knowledge; it was possible to demonstrate that the methods used can be integrated to ICT, resulting in obtaining perceptual relationships between researchers and expressing the groups formed from clusters of observations in each subcategory and knowledge domains of the two case studies analyzed.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[análisis de conglomerados]]></kwd>
<kwd lng="es"><![CDATA[perfiles de usuario]]></kwd>
<kwd lng="es"><![CDATA[modelo de espacio vectorial]]></kwd>
<kwd lng="en"><![CDATA[cluster analysis]]></kwd>
<kwd lng="en"><![CDATA[user profiles]]></kwd>
<kwd lng="en"><![CDATA[vector space model]]></kwd>
</kwd-group>
</article-meta>
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