Fuzzy c-means (FCM) isa useful clustering technique. Recent modifications o
f FCM using L-1 norm distances increase robustness to outliers. Object and
relational data versions of FCM clustering are defined for the more general
case where the L-p norm (p greater than or equal to 1) or semi-norm (0 < p
< 1) is used as the measure of dissimilarity. We give simple (though compu
tationally intensive) alternating optimization schemes for all object data
cases of p > 0 in order to facilitate the empirical examination of the obje
ct data models. Both object and relational approaches are included in a num
erical study.