<?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>2477-9105</journal-id>
<journal-title><![CDATA[Perfiles]]></journal-title>
<abbrev-journal-title><![CDATA[Perfiles]]></abbrev-journal-title>
<issn>2477-9105</issn>
<publisher>
<publisher-name><![CDATA[Escuela Superior Politécnica de Chimborazo]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2477-91052021000100024</article-id>
<article-id pub-id-type="doi">10.47187/perf.v1i25.118</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[APLICACIÓN DEL ALGORITMO K-MEDOID PARA LA SEGMENTACIÓN DE LOS ALUMNOS INGRESANTES DE UNA UNIVERSIDAD.]]></article-title>
<article-title xml:lang="en"><![CDATA[Application of the k-medoid algorithm for the segmentation of entering students at a university.]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Chavez Valderrama]]></surname>
<given-names><![CDATA[Ledvir Ayrton Walter]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Salinas Flores]]></surname>
<given-names><![CDATA[Jesús Walter]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional Agraria La Molina Facultad de Economía y Planificación Departamento de Estadística e Informática]]></institution>
<addr-line><![CDATA[Lima ]]></addr-line>
<country>Perú</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<volume>1</volume>
<numero>25</numero>
<fpage>24</fpage>
<lpage>29</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_arttext&amp;pid=S2477-91052021000100024&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_abstract&amp;pid=S2477-91052021000100024&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_pdf&amp;pid=S2477-91052021000100024&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Actualmente, en el área de educación superior se ha vuelto indispensable la gestión de los datos para la toma de decisiones académicas y la mejora de los procesos educativos, para ello la analítica y estadística han sido llevados al ámbito tecnológico, donde prima la automatización de procesos y la gestión de grandes bases de datos a través de algoritmos de Machine Learning, uno de los más utilizados son los algoritmos clustering, cuyo propósito es agrupar datos por similitud. El presente estudio tuvo como objetivo encontrar tipos de estudiantes universitarios respecto a sus variables sociodemográficas, económicas y de rendimiento académico, utilizando el algoritmo K-medoid en datos de alumnos ingresantes a la Universidad Nacional Agraria La Molina de Lima, Perú. Se pudo determinar que los ingresantes en estudio se pueden segmentar en 3 grupos, cada uno con características propias, lo que permitirá impulsar cambios a favor de la calidad educativa y promover la renovación de los espacios de enseñanza de manera personalizada en torno al tipo de estudiante que la universidad gestiona.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Currently, in the area within higher education, data management has become essential for academic decision making and the improvement of educational processes. Analytics and statistics have been taken to the technological field, where the processes automation and the large databases management through Machine Learning algorithms are the most used, among which are the clustering algorithms, whose purpose is to group data by similarity. The objective of this study was to find types of university students with respect to their sociodemographic, economic and academic performance variables, using the K-medoid algorithm on data of students entering the Universidad Nacional Agraria La Molina in Lima, Peru. It was determined that the students under study can be segmented into 3 groups, each with its own characteristics, which will make it possible to promote changes in favor of educational quality and promote the renovation of teaching spaces in a personalized way around the type of student that the university manages.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Perfil del ingresante]]></kwd>
<kwd lng="es"><![CDATA[algoritmos de agrupamiento]]></kwd>
<kwd lng="es"><![CDATA[segmentación]]></kwd>
<kwd lng="es"><![CDATA[K-medoid]]></kwd>
<kwd lng="en"><![CDATA[Admitted student profile]]></kwd>
<kwd lng="en"><![CDATA[clustering algorithms]]></kwd>
<kwd lng="en"><![CDATA[segmentation]]></kwd>
<kwd lng="en"><![CDATA[K-medoid]]></kwd>
</kwd-group>
</article-meta>
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