Advantages of Consensus Clustering
- Provides for a method to represent the consensus across multiple runs of a clustering algorithm, to determine the number of clusters in the data, and to assess the stability of the discovered clusters.
- The method can also be used to represent the consensus over multiple runs of a clustering algorithm with random restart (such as K-means, model-based Bayesian clustering, SOM, etc.), so as to account for * its sensitivity to the initial conditions.
- It also provides for a visualization tool to inspect cluster number, membership, and boundaries.
- We will be able to extract lot of features / attributes from multiples runs of different clustering algorithms on the data. These features can give us valuable information in doing a final consensus clustering.
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