Cluster ordered dither

And the answer is 2.

Next I want to focus for a little while on the notion of a clustering coefficient.

So there's a fair amount of interconnection between a node's neighbors.

So just getting it correct to a couple of digits is probably sufficient.

So we'd like to have a way of getting a pretty good answer in time a lot less than this.

And if you do that, you end up with K minus one clusters instead of k clusters.

Suppose we're given the following data points in a 2 dimensional space.

I actually have some friends who work on this too.

Suppose you are given data like this, and you wish to learn that there's 2 clusters a cluster over here and a cluster over here.

I am going to say that the clustering coefficient for graph is just the average of the clustering coefficients of the nodes in the graph.

And second, to narrow this down, it feels that there are clusters of data the way I do it.

K Means is an iterative algorithm and it does two things.

C6 equals to and similarly well c10 equals, too, right?

So just to remind you, here's some code for computing the clustering coefficient of a graph with respect to a particular node v.

So as a concrete example, let's say that one of my cluster centroids, let's say cluster centroid two, has training examples, you know, 1, 5, 6, and 10 assigned to it.

let's go through how this is often defined.

The last question asked, what is the maximum clustering coefficient for a node in B?

I'm going to do. The first step is to randomly initialize two points, called the cluster centroids.

Next is lower case k.

All the data points on the left correspond to the red cluster, and the ones on the right to the green cluster.

K means is a very simple almost binary algorithm that allows you to find cluster centers.

Step 1 Guess cluster centers at random, as shown over here with the 2 stars.

Let me illustrate this once again with the data set that has a different spectral clustering than a conventional clustering.

So, let me do that now.

And so in the earlier diagram, the cluster centroids corresponded to the

It is not the perfectly flat system like the disk of the Milky Way, but it is flat.

K means estimates for a fixed number of k. Here k 2.

So the optimal cluster center would be the halfway point in the middle.

Order them from the lowest clustering coefficient to the highest clustering coefficient.

let mu k be the average of the points assigned to the cluster.

It's likely somewhere over here where I drew the red dot.

Who formatted this thing? And they used clusters instead of physical sectors for allocations,

If we use k means, we shouldn't increase the number of cluster centers.

So, these two crosses here, these are called the Cluster Centroids and I have two of them because I want to group my data into two clusters.

In the M step we now figure out where these parameters should have been.

Suppose we are giving you 4 data points, as indicated by those circles.

I'm going to color them either red or blue.

For this problem we have 6 graphs, and we want you to order them with a clustering coefficient of the red node of each graph.

Two times the number of links divided by length of neighbors times length of neighbors minus one.

This one over here, we have a constant cost per cluster is new.

Both are prone to local minima.

So, if you could see stars as well, they would look somewhat like this but more condensed.

K of the cluster centroid closest to Xl.

So my question is, from what you understand, do you think that EM or K means we would do a great job finding those clusters or do you think they will likely fail to find those clusters?