Clustering algorithms can be used with many types of data, as long as you have means to distribute them in a space, where there is the concept of distance. Vectors are obvious choices, but not everything can be represented into N-dimensional points. Another way to plot data, that is much closer to real data, is to allow for a large number of binary axis, like tags. So, you can cluster by the amount of tags the entries share, with the distance being (only relative to others) the proportion of these against the non-sharing tags.
An example of tag clustering can be viewed on Google News, an example of clustering on Euclidean spaces can be viewed on the image above (full code here). The clustering code is very small, and the result is very impressive for such a simple code. But the devil is in the details…
Each red dots group is generated randomly from a given central point (draws N randomly distributed points inside a circle or radius R centred at C). Each centre is randomly placed, and sometimes their groups collide (as you can see on the image), but that’s part of the challenge. To find the groups, and their centres, I throw random points (with no knowledge of the groups’ centres) and iterate until I find all groups.
The iteration is very simple, and consists of two steps:
- Assignment Step: For each point, assign it to the nearest mean. This is why you need the concept of distance, and that’s a tricky part. With Cartesian coordinates, it’s simple.
- Update Step: Calculate the real mean of all points belonging to each mean point, and update the point to be at it. This is basically moving the supposed (randomly guessed) mean to it’s rightful place.
On the second iteration, the means, that were randomly selected at first, are now closer to a set of points. Not necessarily points in the same cluster, but the cluster that has more points assigned to any given mean will slowly steal it from the others, since it’ll have more weight when updating it on step 2.
If all goes right, the means will slowly move towards the centre of each group and you can stop when the means don’t move too much after the update step.
Many problems will arise in this simplified version, for sure. For instance, if the mean is exactly in between two groups, and both pull it to their centres with equally strong forces, thus never moving the mean, thus the algorithm thinks it has already found its group, when in fact, it found two. Or if the group is so large that it ends up with two or more means which it belongs, splitting it into many groups.
To overcome these deficiencies, some advanced forms of K-means take into account the shape of the group during the update step, sometimes called soft k-means. Other heuristics can be added as further steps to make sure there aren’t two means too close to each other (relative to their groups’ sizes), or if there are big gaps between points of the same group, but that kind of heuristics tend to be exponential in execution, since they examine every point of a group in relation to other points of the same group.
All in all, still an impressive performance for such a simple algorithm. Next in line, I’ll try clustering data distributed among many binary axis and see how k-means behave.