Whether it's k-clustering, or any kind of hierarchical clustering, the world we live in is still the world of ``I don't like you'' clustering, where the geometric landscape is defined by distances between points. Consider the following example:
There is an intuitive sense in which the first clustering is not ``real'' and the second one is: the idea of 'well-separatedness'' is a pervasive component of how a good clustering is perceived. But if we only measure distances between points, and only measure the cost of a clustering in terms of how costly each cluster is, we'll never be able to distinguish between these two examples.
What's needed is a way of declaring likes (similarity) as well as dislikes (distance), and then, critically:
penalizing similar items in different clusters AS WELL AS different items in the same cluster.
By that measure, we'd be able to distinguish the first and second clusterings, because in the first case, presumably elements close to each other that lie in different clusters will make the clustering look more expensive. This point is worth reiterating. Unless we have some way of penalizing mis-separations as well as mis-groupings, we'll always be at the mercy of the tradeoff between k and cost.
Continuing the "clustering via self-help metaphors" theme to these essays, I call this the "I don't like you, but I like them" way of modelling data.
This is where correlation clustering enters the picture. The correlation clustering model is as follows: every pair of elements is assigned a 1 or -1, encoding a similarity or dissimilarity. The goal is to find a clustering (note: no k!) in which any pair of points in a cluster is penalized for being dissimilar, and any pair of points in two different clusters is penalized for being similar. This can be generalized to arbitrary weights: for each pair of elements you assign weights w+ and w-, with the possible caveat that w+ + w- = 1.
So now the goal is merely to minimize the cost of a clustering. The elegance of correlation clustering lies in the natural way that clusters merge or split depending on the number of similar or dissimilar pairs. Do note though that you need to have input data that can be written in that manner: thresholding a distance function will give you the desired input, but is an ad hoc way of doing it, since the thresholding is arbitrary.
There are a number of different algorithms for correlation clustering, and also a very simple one that yields a good approximation guarantee: pick a point at random, and pull in all its neighbors (all points similar to it). Repeat this process with a new unpicked point, until all points have been selected. This algorithm gives a 3-approximation for correlation clustering.
Correlation clustering is also useful as a way of combining clusterings. We'll talk about this later (ed: how many times have I said that !), but the problem of conensus clustering is to ``cluster the clusterings'', or aggregate them into a single average clustering. An easy way to see the connection is this: given a collection of clusterings of a data set, create an instance of correlation clustering with the positive weight for a pair corresponding to the fraction of clusterings that ``vote'' for that pair being in the same cluster, and the negative weight being the fraction of clusterings that ``vote'' for that pair being separated.