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==Related work==
1. '''Clustering ensemble (Strehl and Ghosh)''': They considered various formulations for the problem, most of which reduce the problem to a hyper-graph partitioning problem. In one of their formulations they considered the same graph as in the correlation clustering problem. The solution they proposed is to compute the best ''k''-partition of the graph, which does not take into account the penalty for merging two nodes that are far apart{{citation needed}}.
2. '''Clustering aggregation (Fern and Brodley)''': They applied the clustering aggregation idea to a collection of [[soft clustering]]s they obtained by random projections. They used an agglomerative algorithm and did not penalize for merging dissimilar nodes{{citation needed}}.
3. '''Fred and Jain''': They proposed to use a single linkage algorithm to combine multiple runs of the ''k''-means algorithm{{citation needed}}.
4. '''Dana Cristofor and Dan Simovici''': They observed the connection between clustering aggregation and clustering of categorical data. They proposed information theoretic distance measures, and they propose [[genetic algorithm]]s for finding the best aggregation solution.
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