metaclustering
Metaclustering is a data analysis technique in unsupervised learning in which the results of multiple clustering analyses are combined or reorganized to produce higher-level groupings. In practice, metaclustering can mean clustering the clusters produced by different algorithms or parameter settings, effectively clustering the results of several base clusterings. It is related to ensemble clustering or consensus clustering, where robustness and stability are emphasized.
There are two main flavors. In the co-association approach, many clustering runs are executed on the same
Applications span bioinformatics, imaging, and marketing, including identification of robust gene modules in microarray or RNA-seq
Metaclustering complements other ensemble techniques and provides a framework for deriving stable, interpretable cluster structures from