A Semi-Supervised Framework for MMMs-Induced Fuzzy Co-Clustering with Virtual Samples. (23rd June 2016)

• Record Type: Journal Article
• Authors: Tanaka, Daiji; Honda, Katsuhiro; Ubukata, Seiki; Notsu, Akira
• Other Names: Kilic Kemal Academic Editor.
• Is Part Of: Advances in fuzzy systems. Volume 2016(2016)
• Publication Date: 2016-06-23
• Subjects: Fuzzy systems -- Periodicals; Systèmes flous; Fuzzy systems; Periodicals; 511.313
• DOI: 10.1155/2016/5206048
• Languages: English
• ISSNs: 1687-7101

Abstract

Abstract : 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.