Weighting quantitative and qualitative variables in clustering methods
Description of individuals in ill-structured domains produces messy data matrices. In this context, automated classification requires the management of those kind of matrices. One of the features involved in clustering is the evaluation of distances between individuals. Then, a special function to c...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 1997 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/3494 |
| Acceso en línea: | https://hdl.handle.net/2099/3494 |
| Access Level: | acceso abierto |
| Palabra clave: | Clustering Metrics Qualitative and quantitative variables Messy data Ill-structured domaines Sistemes de control Classificació AMS::93 Systems Theory Control::93C Control systems, guided systems |
| Sumario: | Description of individuals in ill-structured domains produces messy data matrices. In this context, automated classification requires the management of those kind of matrices. One of the features involved in clustering is the evaluation of distances between individuals. Then, a special function to calculate distances between individuals partially simultaneously described by qualitative and quantitative variables is required. In this paper properties and details of the metrics used by Klass in this situation is presented --- Klass is a clustering system oriented to the classification of ill-structured domains which implements an adapted version of the reciprocal neighbors algorithm; it also takes advantage of any additional information that an expert can provide about the target concepts. |
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