<?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>2227-1899</journal-id>
<journal-title><![CDATA[Revista Cubana de Ciencias Informáticas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev cuba cienc informat]]></abbrev-journal-title>
<issn>2227-1899</issn>
<publisher>
<publisher-name><![CDATA[Editorial Ediciones Futuro]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2227-18992020000300018</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Nuevo método para el descubrimiento de subgrupos no redundantes]]></article-title>
<article-title xml:lang="en"><![CDATA[A new method for not redundant Subgroup Discovery]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bravo Ilisastigui]]></surname>
<given-names><![CDATA[Lisandra]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martín Rodriguez]]></surname>
<given-names><![CDATA[Diana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García Borroto]]></surname>
<given-names><![CDATA[Milton]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Tecnológica de la Habana José Antonio Echeverría, CUJAE Facultad de Informática ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2020</year>
</pub-date>
<volume>14</volume>
<numero>3</numero>
<fpage>18</fpage>
<lpage>40</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2227-18992020000300018&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2227-18992020000300018&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2227-18992020000300018&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El descubrimiento de subgrupos es una tarea de la Minería de Datos que tiene como objetivo identificar subconjuntos de ejemplos con un comportamiento inusual con respecto a una característica de interés. Un problema que puede afectar la comprensibilidad de los modelos obtenidos por los métodos de descubrimiento de subgrupos, es la redundancia. En este artículo se presenta DINOS, un algoritmo para la extracción de subgrupos no redundantes y con alta inusualidad en forma de reglas cuantitativas. Para ello se emplea un algoritmo genético multiobjetivo que permite optimizar inusualidad, sensibilidad, confianza y comprensibilidad, mientras realiza un aprendizaje evolutivo de los intervalos de los atributos que intervienen en las reglas de subgrupos. Además, este algoritmo emplea criterios para determinar redundancia basados en cobertura y los intervalos de confianza del oddratio, para filtrar los subgrupos redundantes. Un estudio experimental basado pruebas no paramétricas y una comparación pareada con los modelos obtenido por varios algoritmos del estado del arte demuestra la novedad y validez de la propuesta. Los resultados muestran que DINOS obtiene los mejores valores en las métricas estudiadas entre los algoritmos involucrados en el estudio.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Subgroup Discovery is a Data Mining task to identify descriptions of subsets of a data set that show an interesting behavior with respect to certain interestingness criteria. A major problem that affect the comprehensibility of the results is the redundancy feature. Dependencies between the non-target attributes lead to large numbers of variations of a particular subgroup. Since many descriptions can have a similar coverage of the given data. In this work a new algorithm for the description induction of not overlapped subgroups (DINOS) is proposed. This algorithms is able to automatically determine the intervals for the numerical attributes of a data set. The experimental study shows that DINOS obtains subgroups with high quality that improves the results reported in the literatura.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Descubrimiento de Subgrupos]]></kwd>
<kwd lng="es"><![CDATA[redundancia]]></kwd>
<kwd lng="es"><![CDATA[algoritmo genético]]></kwd>
<kwd lng="en"><![CDATA[Subgroup Discovery]]></kwd>
<kwd lng="en"><![CDATA[redundancy]]></kwd>
<kwd lng="en"><![CDATA[genetic algorithm]]></kwd>
</kwd-group>
</article-meta>
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