A fuzzy co-clustering model for three-modes relational cooccurrence data
Katsuhiro Honda, Yurina Suzuki, Mio Nishioka, Seiki Ubukata, Akira Notsu · 2017
Fuzzy co-clustering is a basic technique for analyzing co-cluster structures in cooccurrence information among objects and items. When we have not only cooccurrence information among objects and items but also intrinsic relation among items and other ingredients, it is expected that we can find more useful co-cluster structures among three-modes cooccurrence relation. In this paper, the conventional fuzzy clustering for categorical multivariate data (FCCM) algorithm is extended by utilizing three-types of fuzzy memberships for objects, items and ingredients, where the aggregation degree of three elements in each co-cluster is maximized through iterative updating of memberships. The characteristic features of the proposed method are demonstrated through several numerical experiments including a school lunch calendar analysis.