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Title: Self-semi-supervised clustering for large scaled-data with a massive null cluster Authors:  Johan Lim - Seoul National University (Korea, South) [presenting]
Soohyun Ahn - Ajou University (Korea, South)
hyungwon Choi - National University of Singapore (Singapore)
Kyeong Eun Lee - Kyungpook National University (Korea, South)
Abstract: Self-semi-supervised clustering, a new clustering method for large scale data with a massive null group, is proposed. Self-semi-supervised clustering is a two-stage procedure: preselect a part of ``null'' group from the data in the first stage and apply semi-supervised clustering to the rest of the data in the second stage, allowing them to be assigned to the null group. We evaluate the performance of the proposed method using a simulation study and demonstrate the method in the analysis of time course gene expression data from a longitudinal study of Influenza A virus infection.