Mining Robust Overlapping Co-Clustering in the Presence of Noise

Data clustering techniques have been applied to extract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great achievement. However, due to the various structures and intensive noise, there is no reliable clustering approach can be applied to all gene expression data. In this paper, the problem of revealing robust overlapping co-clustering is identified in the presence of noise. Instead of requiring all objects in a cluster have identical attribute order, this system requires that (1) at least a certain fraction of the objects have identical attribute order; (2) other objects in the cluster may deviate from the consensus order by up to a certain fraction of attributes.