Clustering multiple data sets using consensus clustering

Clustering methods are usually applied to a single data set of interest. However, several related data sets can sometimes be available. In this case it is often of interest to determine whether the features of a single data set identified by a cluster analysis are common across all data sets. Consensus clustering has been proposed as one such way to combine cluster information from multiple data sources. See here and here for more details. The project would involve reviewing methods for consensus clustering and implementing these methods on real and simulated data.