Species Clustering via Classical and Interval Data Representation
CHAVENT, Marie
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
CHAVENT, Marie
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
< Reduce
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Language
en
Chapitre d'ouvrage
This item was published in
Selected Contributions in Data Analysis and Classification, Selected Contributions in Data Analysis and Classification. 2007p. 183-191
Springer Berlin Heidelberg
English Abstract
Consider a data table where n objects are described by p numerical variables and a qualitative variable with m categories. Interval data representation and interval data clustering methods are useful for clustering the m ...Read more >
Consider a data table where n objects are described by p numerical variables and a qualitative variable with m categories. Interval data representation and interval data clustering methods are useful for clustering the m categories. We study in this paper a data set of fish contaminated with mercury. We will see how classical or interval data representation can be used for clustering the species of fish and not the fish themselves. We will compare the results obtained with the two approaches (classical or interval) in the particular case of this application in Ecotoxicology.Read less <
Origin
Hal imported