Abstract
Fuzzy c-means (FCM) is a useful clustering technique. Recent modifications of FCM using L1 norm distances increase robustness to outliers. Object and relational data versions of FCM clustering are defined for the more general case where the Lp norm (p≥1) or semi-norm (0<p<1) is used as the measure of dissimilarity. We give simple (though computationally intensive) alternating optimization schemes for all object data cases of p>0 in order to facilitate the empirical examination of the object data models. Both object and relational approaches are included in a numerical study.
Original language | English |
---|---|
Pages (from-to) | 576-582 |
Number of pages | 7 |
Journal | IEEE Transactions on Fuzzy Systems |
Volume | 8 |
Issue number | 5 |
DOIs | |
State | Published - Oct 2000 |