A semi-supervised fuzzy co-clustering framework and application to twitter data analysis
Katsuhiro Honda, Seiki Ubukata, Akira Notsu, Norimitsu Takahashi, Yutaka Ishikawa · 2015
Semi-supervised clustering is an efficient scheme for utilizing data with partial class information, where unsupervised data distributions are estimated under some supports of partial supervised class information. In this paper, a novel framework for performing fuzzy co-clustering of cooccurrence information with partial supervision is proposed, which is induced by multinomial mixture concept. Co-clustering is useful for extracting object-item pair-wise clusters from cooccurrence information and has been utilized in various applications such as document-keyword analysis and customer-products purchase history data analysis. Several experimental results including a twitter data analysis demonstrate the ability of improving the classification quality of the fuzzified co-cluster structural knowledge. Then, the proposed semi-supervised framework is expected to be a powerful tool in Big Data analysis with huge volumes of data but partial supervisions only.