Trimmed fuzzy clustering for interval-valued data

In this paper, following a partitioning around medoids approach, a fuzzy clustering model for interval-valued data, i.e., FCMd-ID, is introduced. Successively, for avoiding the disruptive effects of possible outlier interval-valued data in the clustering process, a robust fuzzy clustering model with a trimming rule, called Trimmed Fuzzy <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$C$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi>C</mi> </math> </EquationSource> </InlineEquation>-medoids for interval-valued data (TrFCMd-ID), is proposed. In order to show the good performances of the robust clustering model, a simulation study and two applications are provided. Copyright Springer-Verlag Berlin Heidelberg 2015