Mining gradual dependencies with variation strength
In this paper we propose a definition of gradual dependence as a fuzzy association rule. Gradual dependencies represent tendencies in the variation of the degree of fulfilment of properties in a set of objects. We define the degree of variation of a certain imprecise property for a pair of objects a...
| Autores: | , , , |
|---|---|
| Formato: | artículo |
| Fecha de publicación: | 2008 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/13157 |
| Acesso em linha: | https://hdl.handle.net/2099/13157 |
| Access Level: | acceso abierto |
| Palavra-chave: | Artificial intelligence Gradual dependencies Gradual rules Aproximate dependencies Association rules Intel•ligència artificial Classificació AMS::68 Computer science::68T Artificial intelligence Àrees temàtiques de la UPC::Informàtica::Informàtica teórica |
| Resumo: | In this paper we propose a definition of gradual dependence as a fuzzy association rule. Gradual dependencies represent tendencies in the variation of the degree of fulfilment of properties in a set of objects. We define the degree of variation of a certain imprecise property for a pair of objects as the difference between their membership degrees to the fuzzy set defining the property. When considering a transaction for every pair of objects and considering items representing positive and negative variations foer each property of interest, fuzzy association rules become gradual dependencies and the accuray and support of the former can be employed to assess the corresponding dependencies. We study the new semantics and properties of the resulting fuzzy gradual dependence, and we propose a way to adapt existing fuzzy association rule mining algorithms for the new task of mining such dependencies |
|---|