Efficient Consensus Functions

**Cluster-based Similarity Partitioning Algorithm (CSPA).**- A clustering signifies a relationship between objects in the same
cluster and can thus be used to establish a measure of pairwise
similarity. This
*induced similarity measure*is then used to recluster the objects, yielding a combined clustering. **HyperGraph Partitioning Algorithm (HGPA).**- In this algorithm, we approximate the maximum mutual information
objective with a constrained
*minimum cut*objective. Essentially, the cluster ensemble problem is posed as a partitioning problem of a suitably defined hypergraph where hyperedges represent clusters. **Meta-CLustering Algorithm (MCLA).**- Here, the objective of integration is viewed as a
*cluster correspondence problem*. Essentially, groups of clusters (meta-clusters) have to be identified and consolidated.

- Representing Sets of Clusterings as a Hypergraph
- Cluster-based Similarity Partitioning Algorithm (CSPA)
- HyperGraph Partitioning Algorithm (HGPA)
- Meta-CLustering Algorithm (MCLA)
- Discussion and Comparison