K-means-based Consensus Clustering and its Applications Speaker: Hongfu Liu | Advisor: Yun Fu
Consensus clustering aims to find a single clustering which agrees with several basic partitions as much as possible, which recently attracts increasing attentions. In this talk, I will introduce some basic concepts of consensus clustering and some advanced algorithms. All these algorithms are K-means-based, which are of high efficiency and robustness. Some applications on document clustering, gene expression analysis, constrained clustering are also included.
Bio:
Hongfu Liu received his B.E. and master degree in Management Information Systems from the School of Economics and Management, Beihang University, in 2011 and 2014, respectively. He is currently a second-year PhD student in Northeastern University. His research interests generally focus on data mining and machine learning, with special interests in ensemble learning.