• Mohamed Nadif - Head of the "Machine Learning for Data Science" Team, LIPADE laboratory, University of Paris.

    A mini-course on "Co-clustering" will be given.


    In the era of data science, clustering various kinds of objects (documents, genes, customers) has become a key activity and many high quality packaged implementations are provided for this purpose by many popular packages. A natural extension of standard cluster analysis is co-clustering where objects and features are simultaneously grouped into meaningful blocks called co-clusters or biclusters, thus making large datasets easier to handle and interpret. In fact, co-clustering has found applications in many areas such as bio-informatics web mining, text mining and recommender systems. Various co-clustering algorithms have been proposed over the years. The goal of the mini-course is to review popular different approaches to perform co-clustering such as matrix factorization based methods, spectral methods, and model-based methods. Models and algorithms will be presented and illustrated.