Formation of Resemblance Measures Among Sets
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| Format: | Artículo científico |
| Language: | en |
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Instituto Politécnico Nacional
2006
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| _version_ | 1876444181951414272 |
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| author | Otar Verulava |
| author_facet | Otar Verulava |
| contents | Formation of Resemblance Measures Among Sets Otar Verulava Ramaz Khurodze Lasha Verulava Computación Clusterization Metric Functions Pattern Recognition In this paper it is described a method to compute the distance between sets, that implies the formation of distance functions different from Hausdorff metric. Two functions with metric properties, which describe quantitatively distances between sets, are formed. First function can be used for sets arbitrary situated from each other. Second distance is more suited for sets clustered by rank links. For reconstructing the metric functions, we define the so-called boundary points between sets. This allows to defining the minimal and the maximal distances between them, which represents the arguments for the formed metric functions. This also allows quantitatively estimate in amore complete way the isolation degree between given sets. 2006 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61590302 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.9 |
| format | Artículo científico |
| id | redalyc_61590302 |
| institution | Redalyc |
| language | en |
| publishDate | 2006 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Formation of Resemblance Measures Among Sets Otar Verulava Computación Clusterization Metric Functions Pattern Recognition Formation of Resemblance Measures Among Sets Otar Verulava Ramaz Khurodze Lasha Verulava Computación Clusterization Metric Functions Pattern Recognition In this paper it is described a method to compute the distance between sets, that implies the formation of distance functions different from Hausdorff metric. Two functions with metric properties, which describe quantitatively distances between sets, are formed. First function can be used for sets arbitrary situated from each other. Second distance is more suited for sets clustered by rank links. For reconstructing the metric functions, we define the so-called boundary points between sets. This allows to defining the minimal and the maximal distances between them, which represents the arguments for the formed metric functions. This also allows quantitatively estimate in amore complete way the isolation degree between given sets. 2006 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61590302 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.9 |
| title | Formation of Resemblance Measures Among Sets |
| topic | Computación Clusterization Metric Functions Pattern Recognition |
| url | https://www.redalyc.org/articulo.oa?id=61590302 |