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.

Cluster ordered dither

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

And the answer is 2.

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

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

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.

I actually have some friends who work on this too.

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

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.

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?

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Mark young's projects.

let's go through how this is often defined.

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

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.

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.

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

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