A Semi-Supervised Framework for MMMs-Induced Fuzzy Co-Clustering with Virtual Samples

الملخص EN

Although the goal of clustering is to reveal structural information from unlabeled datasets, in cases with partial structural supervisions, semi-supervised clustering is expected to improve partition quality.

However, in many real applications, it may cause additional costs to provide an enough amount of supervised objects with class labels.

A virtual sample approach is a practical technique for improving classification quality in semi-supervised learning, in which additional virtual samples are generated from supervised objects.

In this research, the virtual sample approach is adopted in semi-supervised fuzzy co-clustering, where the goal is to reveal object-item pairwise cluster structures from cooccurrence information among them.

Several experimental results demonstrate the characteristics of the proposed approach.