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Descripción
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| In this work we take the concept of typicality from the cognitive and psychological point of view, and we apply their meaning to the interpretation of numerical data through fuzzy clustering algorithms. With the PFCM clustering algorithm, based on the Fuzzy c-Means clustering algorithm (FCM), we get a relative typicality (membership degree), and, also based on the Possibilitistic c- Means (PCM), an absolute typicality (typicality value). The results clearly show the advantages of the information obtained about the data set used, taking into account the different meaning of typicalities and the availability of both values with the clustering algorithm used | |
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Internacional
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Si |
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JCR del ISI
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No |
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Título de la revista
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Research on Computer Sciences S Issue: Advances in Pattern Recognition |
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ISSN
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1870-4069 |
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Factor de impacto JCR
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0 |
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Información de impacto
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Volumen
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44 |
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DOI
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Número de revista
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0 |
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Desde la página
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157 |
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Hasta la página
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166 |
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Mes
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JUNIO |
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Ranking
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