Two clustering problems are considered. We consider a lot of different clusters of the same data for a given number of clusters. Data clustering is understood as their stable partition into a given number of sets. Clustering is considered stable if the corresponding partitioning remains unchanged with its minimum change. How to create a new cluster based on ensemble clusterings? The second problem is the following. A definition of the committee synthesis as ensemble clustering is introduced. The sets of best and worst matrices of estimates are considered. Optimum clustering is built on the basis of the clusterings obtained as being closest to the set of the best estimation matrices or as the most distant from the set of worst-case matrices of estimates. As a result, the problem of finding the best committee clustering is formulated as a discrete optimization problem on permutations.
Part of the book: Recent Applications in Data Clustering