A study on fuzzy co-clustering with partial supervision and virtual samples
Daiji Tanaka, Katsuhiro Honda, Seiki Ubukata, Akira Notsu · 2015
Semi-supervised clustering is a promising approach for improving partition quality of unsupervised clustering in large-scale data analysis while it is often difficult to utilize an enough amount of supervised objects. A virtual sample approach is a practical technique for improving classification quality in semi-supervised learning, in which additional virtual samples are generated by combining several supervised objects. In this research, the virtual sample approach is adopted in semi-supervised fuzzy co-clustering and its characteristics are demonstrated through a numerical experiment.