Species Clustering via Classical and Interval Data Representation

Marie Chavent (1, 2)

1 IMB - Institut de Mathématiques de Bordeaux
2 CQFD - Quality control and dynamic reliability

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.

Domaines: Applications [stat.AP]; Autres [stat.ML]

Marie Chavent. Species Clustering via Classical and Interval Data Representation. Paula Brito, Guy Cucumel, Patrice Bertrand and Francisco de Carvalho. Selected Contributions in Data Analysis and Classification, Springer Berlin Heidelberg, pp.183-191, 2007, Studies in Classification, Data Analysis, and Knowledge Organization, ⟨10.1007/978-3-540-73560-1_17⟩. ⟨hal-00273178⟩