Bi-Clustering, or Co-Clustering, is an extension of the clustering approaches discussed that seeks to simultaneously perform clustering both within the observations and columns of a dataset. A situation where Co-Clustering is often applied is genetic research, where data consists of a number of patient samples (observations) and also many gene expressions (columns) for each patient. In such a scenario, groupings can be discovered through a combination of the rows and columns of the data. One advantage Co-Clustering offers is the ability to discover patterns from only a subset of the full feature space.