New clustering methods for interval data
Two new clustering methods are proposed for multivariate interval data. They are modifications of the dynamic \(k\)-clustering algorithm based on minimization of distances from elements of the cluster to its prototype (centre). The number \(k\) of clusters is fixed. A sum of Hausdorff distances between intervals is used in the first modification and a specific dissimilarity measure based on a discretization of the intervals is used in the second modification. These algorithms are applied to data on monthly minimal and maximal daily average temperatures in 60 meteorological stations in China.
scientific article; zbMATH DE number 5158597