We can represent a data set as a collection of object nodes and a collection of attribute nodes, where there is a link between each object and each attribute, and where the weight of that link is the value of the object for that attribute. For sparse data, if the value is 0, the link is omitted.

Bipartite clustering attempts to partition this graph into disjoint clusters, where each cluster consists of a set of object nodes and a set of attribute nodes.

The objective is to maximize the weight of links between the object and attribute nodes of a cluster, while minimizing the weight of links between object and attribute links in different clusters.

This type of clustering is also known as co-clustering since the objects and attributes are clustered at the same time.

How is bipartite clustering (co-clustering) different from clustering the sets of objects and attributes separately?

Are there any cases in which these approaches yield the same clusters?