Spectral Co-Clustering is an implementation of Co-Clustering that models the input data as a bipartite graph, where the observations and features form two sets of nodes that are connected by edges. Thus, it is an extension of the spectral clustering setup that uses a subset of both the observations and features in the clustering process. A possible use case for spectral Co-Clustering is text classification, where one set of nodes might consist of words found across a set of documents that are linked to the documents in the second set of nodes, where the edges indicate that word w appears within document d.
The website is in Maintenance mode. We are in the process of adding more features.
Any new bookmarks, comments, or user profiles made during this time will not be saved.