Classification of Multivariate Objects Using Interval Quantile Classes

The paper contains a proposal of interval data clustering related to given social and economic objects characterized by many interval variables. This multivariate approach is based on an original conception of interval quantiles constructed using a special definition derived from the notion of the Hausdorff distance. In order to improve the quality of classification, the obtained interval quantile classes can be next aggregated into larger merged classes. The efficiency of our method can be assessed using especially defined indices of entropy and volume coefficients. The second notion replaces the classical concept of area, which is not applicable in this case.