{"id":8295,"date":"2020-03-27T11:59:29","date_gmt":"2020-03-27T10:59:29","guid":{"rendered":"https:\/\/complex-systems-ai.com\/?page_id=8295"},"modified":"2022-12-03T23:04:44","modified_gmt":"2022-12-03T22:04:44","slug":"mesures-de-distance-pour-les-attributs-binaires","status":"publish","type":"page","link":"https:\/\/complex-systems-ai.com\/es\/particionamiento-de-datos\/medidas-de-distancia-para-atributos-binarios\/","title":{"rendered":"Medidas de distancia para atributos binarios"},"content":{"rendered":"<div data-elementor-type=\"wp-page\" data-elementor-id=\"8295\" class=\"elementor elementor-8295\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2c81673 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2c81673\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-8af6dd4\" data-id=\"8af6dd4\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d802e16 elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"d802e16\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/complex-systems-ai.com\/es\/particionamiento-de-datos\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Partici\u00f3n de datos<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-f57d920\" data-id=\"f57d920\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-defb29d elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"defb29d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/complex-systems-ai.com\/es\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Pagina de inicio<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-d5c05a7\" data-id=\"d5c05a7\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cc99004 elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"cc99004\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"http:\/\/www.iiisci.org\/journal\/pdv\/sci\/pdfs\/GS315JG.pdf\" target=\"_blank\" rel=\"noopener\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Wiki<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7e8f9d00 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7e8f9d00\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1d634713\" data-id=\"1d634713\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6118a488 elementor-widget elementor-widget-text-editor\" data-id=\"6118a488\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Contenido<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Tabla de contenido alternativo\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Palanca<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/complex-systems-ai.com\/es\/particionamiento-de-datos\/medidas-de-distancia-para-atributos-binarios\/#Mesures-de-distance-pour-les-attributs-binaires\" >Medidas de distancia para atributos binarios<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Mesures-de-distance-pour-les-attributs-binaires\"><\/span>Medidas de distancia para atributos binarios<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Muchos m\u00e9todos de <a href=\"https:\/\/complex-systems-ai.com\/es\/particionamiento-de-datos\/\">fraccionamiento<\/a> use medidas de distancia para determinar la similitud o diferencia entre cualquier par de objetos (como atributos binarios). Es com\u00fan denotar la distancia entre dos instancias x_i y x_j como: d(x_i, x_j). Una medida de distancia v\u00e1lida debe ser sim\u00e9trica y obtiene su valor m\u00ednimo (normalmente cero) en el caso de vectores id\u00e9nticos. La medida de distancia se denomina medida de distancia m\u00e9trica si tambi\u00e9n satisface las siguientes propiedades:<\/p>\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" class=\"alignnone\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2020\/03\/mesure1.png\" alt=\"Medidas de distancia para atributos binarios\" width=\"389\" height=\"84\" title=\"\"><\/figure>\n\n<p>En el caso de atributos binarios, la distancia entre objetos se puede calcular en base a una tabla de contingencia. Un atributo binario es sim\u00e9trico si sus dos estados tienen el mismo valor. En este caso, el uso del coeficiente de coincidencia simple puede evaluar la diferencia entre dos objetos:<\/p>\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" class=\"alignnone\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2020\/03\/mesure4.png\" alt=\"Medidas de distancia para atributos binarios\" width=\"223\" height=\"52\" title=\"\"><\/figure>\n\n<p>donde q es el n\u00famero de atributos igual a 1 para los dos objetos; t es el n\u00famero de atributos igual a 0 para los dos objetos; ysyr son el n\u00famero de atributos que no son iguales para los dos objetos.<\/p>\n\n<p>Un atributo binario es asim\u00e9trico, si sus estados no son igualmente importantes (el resultado positivo generalmente se considera m\u00e1s importante). En este caso, el denominador ignora las coincidencias negativas sin importancia (t). Esto se llama el coeficiente de <a href=\"https:\/\/complex-systems-ai.com\/es\/particionamiento-de-datos\/funcion-de-similitud\/\">jaccard<\/a> :<\/p>\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" class=\"alignnone\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2020\/03\/mesure5.png\" alt=\"Medidas de distancia para atributos binarios\" width=\"179\" height=\"48\" title=\"\"><\/figure>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>P\u00e1gina de inicio de Wiki de particionamiento de datos Medidas de distancia para atributos binarios Muchos m\u00e9todos de particionamiento utilizan medidas de distancia para determinar... <\/p>","protected":false},"author":1,"featured_media":0,"parent":8271,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-8295","page","type-page","status-publish","hentry"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages\/8295","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/comments?post=8295"}],"version-history":[{"count":8,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages\/8295\/revisions"}],"predecessor-version":[{"id":18974,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages\/8295\/revisions\/18974"}],"up":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages\/8271"}],"wp:attachment":[{"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/media?parent=8295"}],"curies":[{"name":"gracias","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}