For several months we've for been taking a look at cluster analysis which seeks to partition sets of data into subsets of similar data, known as clusters. Most recently we have focused our attention on hierarchical clusterings, which are sequences of sets of clusters in which pairs of data that belong to the same cluster at one step belong to the same cluster in the next step.
A simple way of constructing them is to initially place each datum in its own cluster and then iteratively merge the closest pairs of clusters in each clustering to produce the next one in the sequence, stopping when all of the data belong to a single cluster. We have considered three ways of measuring the distance between pairs of clusters, the average distance between their members, the distance between their closest members and the distance between their farthest members, known as average linkage, single linkage and complete linkage respectively, and implemented a reasonably efficient algorithm for generating hierarchical clusterings defined with them, using a min-heap structure to cache the distances between clusters.
Finally, I claimed that there is a more efficient algorithm for generating single linkage hierarchical clusterings that would make the sorting of clusters by size in our ak.clustering type too expensive and so last time we implemented the ak.rawClustering type to represent clusterings without sorting their clusters which we shall now use in the implementation of that algorithm.

Recently, my fellow students and I constructed a mathematical orrery which modelled the motion of heavenly bodies employing Sir N-----'s laws of gravitation and motion, rather than clockwork, as its engine. Those laws state that bodies are attracted toward each other with a force proportional to the product of their masses divided by the square of the distance between them, that a body will remain at rest or in constant motion unless a force acts upon it, that if a force acts upon it then it will be accelerated in the direction of that force at a rate proportional to its strength divided by its mass and that, if so, it will reciprocate with an opposing force of equal strength.
Its operation was most satisfactory, which set us to wondering whether we might use its engine to investigate the motions of entirely hypothetical arrangements of heavenly bodies and I should now like to report upon our progress in doing so.

Last month we saw how we could efficiently generate hierarchical clusterings, which are sequences of sets of clusters, which are themselves subsets of a set of data that each contain elements that are similar to each other, such that if a pair of data are in the same clustering at one step then they must be in the same clustering in the next which will always be the case if we move from one step to the next by merging the closest pairs of clusters. Specifically, we used our ak.minHeap implementation of the min-heap structure to cache the distances between clusters, saving us the expense of recalculating them for clusters that don't change from one step in the hierarchy to the next.
Recall that we used three different schemes for calculating the distance between a pair of clusters, the average distance between their members, known as average linkage, the distance between their closest members, known as single linkage, and the distance between their farthest members, known as complete linkage, and that I concluded by noting that our algorithm was about as efficient as possible in general but that there is a much more efficient scheme for single linkage clusterings; efficient enough that sorting the clusters in each clustering by size would be the most costly operation and so in this post we shall implement objects to represent clusterings that don't do that.

Salutations Sir R-----! I trust that this fine summer weather has you thirsting for a flagon. And perhaps a wager?

Splendid! Come join me at my table!

I propose a game played as a religious observance by the parishioners of the United Reformed Eighth-day Adventist Church of Cthulhu, the eldritch octopus god that lies dead but dreaming in the drowned city of Hampton-on-Sea.
Several years ago, the Empress directed me to pose as a peasant and infiltrate their temple of Fhtagn in the sleepy village of Saint Reatham on the Hill when it was discovered that Bishop Derleth Miskatonic had been directing his congregation to purchase vast tracts of land in the Ukraine and gift them to the church in return for the promise of being spared when Cthulhu finally wakes and devours mankind.

In the previous post we saw how to identify subsets of a set of data that are in some sense similar to each other, known as clusters, by constructing sequences of clusterings starting with each datum in its own cluster and ending with all of the data in the same cluster, subject to the constraint that if a pair of data are in the same cluster in one clustering then they must also be in the same cluster in the next, which are known as hierarchical clusterings.
We did this by selecting the closest pairs of clusters in one clustering and merging them to create the next, using one of three different measures of the distance between a pair of clusters; the average distance between their members, the distance between their nearest members and the distance between their farthest members, known as average linkage, single linkage and complete linkage respectively.
Unfortunately our implementation came in at a rather costly O(n³) operations and so in this post we shall look at how we can improve its performance.

When the Baron last met with Sir R-----, he proposed a wager which commenced with the placing of twenty black tokens and fifteen white tokens in a bag. At each turn Sir R----- was to draw a token from the bag and then put it and another of the same colour back inside until there were thirty tokens of the same colour in the bag, with the Baron winning a coin from Sir R----- if there were thirty black and Sir R----- winning ten coins from the Baron if there were thirty white.
Upon hearing these rules I recognised that they described the classic probability problem known as Pólya's Urn. I explained to the Baron that it admits a relatively simple expression that governs the likelihood that the bag contains given numbers of black and white tokens at each turn which could be used to figure the probability that he should have triumphed, but I fear that he didn't entirely grasp my point.

Last time we implemented the clusterings type to store a set of clustering objects in order to represent hierarchical clusterings, which are sequences of clusterings having the property that if a pair of data are in the same cluster in one clustering then they will be in the same cluster in the next, where clusters are subsets of a set of data that are in some sense similar to each other.
We then went on to define the ak.clade type to represent hierarchical clusterings as trees, so named because that's what they're called in biology when they are used to show the relationships between species and their common ancestors.
Now that we have those structures in place we're ready to see how to create hierarchical clusterings and so in this post we shall start with a simple, general purpose, but admittedly rather inefficient, way to do so.

Last time we met we spoke of my fellow students' and my interest in constructing a model of the motion of heavenly bodies using mathematical formulae in the place of brass. In particular we have sought to do so from first principals using Sir N-----'s law of universal gravitation, which states that the force attracting two bodies is proportional to the product of their masses divided by the square of the distance between them, and his laws of motion, which state that a body will remain at rest or in constant motion unless a force acts upon it, that it will be accelerated in the direction of that force at a rate proportional to its magnitude divided the body's mass and that a force acting upon it will be met with an equal force in the opposite direction.
Whilst Sir N----- showed that a pair of bodies traversed conic sections under gravity, being those curves that arise from the intersection of planes with cones, the general case of several bodies has proved utterly resistant to mathematical reckoning. We must therefore approximate the equations of motion and I shall now report on our first attempt at doing so.

We have so far seen a couple of schemes for identifying clusters in sets of data which are subsets whose members are in some sense similar to each other. Specifically, we have looked at the k means and the shared near neighbours algorithms which implicitly define clusters by the closeness of each datum to the average of each cluster and by their closeness to each other respectively.
Note that both of these algorithms use a heuristic, or rule of thumb, to assign data to clusters, but there's another way to construct clusterings; define a heuristic to measure how close to each other a pair of clusters are and then, starting with each datum in a cluster of its own, progressively merge the closest pairs until we end up with a single cluster containing all of the data. This means that we'll end up with a sequence of clusterings and so before we can look at such algorithms we'll need a structure to represent them.

Ho there Sir R-----! Will you join me for a cold tankard of ale to refresh yourself on this warm spring evening?

And, might I hope, for a little sport?

I should not have doubted it for a moment sir!

This fine weather reminds me of the time I spent as the Empress's trade envoy to the market city of Argos, famed almost as much for the remarkable, if somewhat fragile, mechanical contraptions made by its artificers and the most reasonably priced jewellery sold by its goldsmiths as for its fashion for tiny writing implements.