Noise rejection schemes for FCM-type co-clustering based on uniform noise distribution
Nami Yamamoto, Katsuhiro Honda, Seiki Ubukata, Akira Notsu · 2017
Fuzzy co-clustering is an extension of FCM-type clustering, where the within-cluster-error measure of FCM is replaced by the aggregation degree of two types of fuzzy memberships with the goal being to estimate object-item pairwise clusters from their cooccurrence information. This paper proposes a noise rejection scheme for FCM-type co-clustering models, which is constructed based on the probabilistic co-clustering concept. Noise FCM was achieved by introducing an additional noise cluster into FCM, where the noise cluster was assumed to have a uniform prototype distribution. A similar concept was implemented for probabilistic concept-based co-clustering for robust estimation. The main contribution of this paper is to demonstrate that the uniform distribution concept can also be useful in FCM-type co-clustering models, even though their objective functions are not designed based on probabilistic concepts.