Diferencia entre revisiones de «Índice de Czekanowski»

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Línea 6: Línea 6:
 
d = spp ausentes en común
 
d = spp ausentes en común
  
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[http://people.revoledu.com/kardi/tutorial/Similarity/SimpleMatching.html Tutorial sobre medición de similitud]
  
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[http://media.wiley.com/product_data/excerpt/61/04714696/0471469661.pdf Tutorial incompleto, buscar referencia. Explicación simple sobre índices de similitud y distancia]
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With binary variables, we traditionally focus on the notion of similarity rather
 +
than distance (or dissimilarity). Consider two binary vectors x and y that consist
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of two strings [xk], [yk] of binary data; compare them coordinatewise and do the
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simple counting of occurrences:
 +
number of occurrences when xk and yk are both equal to 1
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number of occurrences when xk = 0 and yk = 1
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number of occurrences when xk = 1 and yk = 0
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number of occurrences when xk and yk are both equal to 0
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These four numbers can be organized in a 2 by 2 co-occurrence matrix (contingency
 +
table) that visualizes how “close” these two strings are to each other.
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::1 0
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::1 a b
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::0 c d
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Evidently the zero nondiagonal entries of this matrix point at the ideal matching
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(the highest similarity). Based on these four entries, there are several commonly
 +
encountered measure of similarity of binary vectors x and y. The simplest matching coefficient computes as the following ratio:
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a + d
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a + b + c + d
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(1.4)
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The Russell and Rao measure of similarity consists of the quotient
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a
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a + b + c + d
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(1.5)
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The Jacard index involves the case when both inputs assume values equal to 1:
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a
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a + b + c
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(1.6)
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The Czekanowski index is practically the same as the Jacard index, but by adding
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the weight factor of 2, it emphasizes the coincidence of situations where entries
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of x and y both assume values equal to 1:
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2a
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2a + b + c
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(1.7)
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-->
 
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[[Categoría:Glosario]] [[Categoría:Esbozo]]

Revisión del 21:35 1 oct 2006

Cz =

a = ssp comunes b = exclusivas de grupo 1 c = exclusivas de grupo 2 d = spp ausentes en común

Tutorial sobre medición de similitud

Tutorial incompleto, buscar referencia. Explicación simple sobre índices de similitud y distancia