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# Correlation clustering

In machine learning, correlation clustering or cluster editing operates in a scenario where the relationship between the objects are known instead of the actual representation of the objects. For example, given a signed graph G = (V,E) where the edge label indicates whether two nodes are similar (+) or different (−), the task is to cluster the vertices so that similar objects are grouped together. Unlike other clustering algorithms this does not require choosing the number of clusters k in advance because the objective, to minimize the disagreements, is independent of the number of clusters.

It may not be possible to find a perfect clustering, where all similar items are in a cluster while all dissimilar ones are in different clusters. If the graph indeed admits a perfect clustering, then simply deleting all the negative edges and finding the connected components in the remaining graph will return the required clusters.

But, in general a graph may not have a perfect clustering. For example, given nodes a,b,c such that a,b and a,c are similar while b,c are dissimilar, a perfect clustering is not possible. In such cases, the task is to find a clustering that maximizes the number of agreements (number of + edges inside clusters plus the number of - edges between clusters) or minimizes the number of disagreements (the number of - edges inside clusters plus the number of + edges between clusters). This problem of maximizing the agreements is NP-complete (multiway cut problem reduces to maximizing weighted agreements and the problem of partitioning into triangles can be reduced to unweighted version)

Bansal et al. discuss the NP-completeness proof and also present both a constant factor approximation algorithm and polynomial-time approximation scheme to find the clusters in this setting. Ailon et al. propose a randomized 3-approximation algorithm for the same problem.

`CC-Pivot(G=(V,E+,E-))`

``````   Pick random pivot i ∈ V
Set C = {i}, V'=Ø
For all j ∈ V, j ≠ i;
If (i,j) ∈ E+ then
Else (If (i,j) ∈ E-)
Let G' be the subgraph induced by V'
Return clustering C,CC-Pivot(G')
```
```

The authors show that the above algorithm is a 3-approximation algorithm for correlation clustering.

## Correlation clustering (data mining)

Correlation clustering also relates to a different task, where correlations among attributes of feature vectors in a high-dimensional space are assumed to exist guiding the clustering process. These correlations may be different in different clusters, thus a global decorrelation cannot reduce this to traditional (uncorrelated) clustering.

Correlations among subsets of attributes result in different spatial shapes of clusters. Hence, the similarity between cluster objects is defined by taking into account the local correlation patterns. With this notion, the term has been introduced in  simultaneously with the notion discussed above. Different methods for correlation clustering of this type are discussed in , the relationship to different types of clustering is discussed in , see also Clustering high-dimensional data.

Correlation clustering (according to this definition) can be shown to be closely related to biclustering. As in biclustering, the goal is to identify groups of objects that share a correlation in some of their attributes; where the correlation is usually typical for the individual clusters.

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