This case has an affinity with that one.

Electron Affinity

Generic Affinity

I had an affinity for motercycles when I was young.

Generic affinity of this object

So in summary, spectral clustering builds an affinity matrix of the data points.

In that time, I developed an affinity for your world and its people.

I feel a strong affinity for her.

They have a strange affinity for each other.

So, how do I explain this mysterious affinity?

This is called affinity based clustering or spectral clustering.

The fundamental idea of spectral clustering is to cluster by affinity.

So spectral clustering gets annotation of affinity to make clustering happen.

But realtive to all the other points, the affinity is weak.

I then calculate for each pair of data points their affinity, where I use for now affinity as the quadratic distance in this diagram over here.

Open individuals have an affinity for liberal, progressive, left wing political views they like a society which is open and changing

Select the first of the three starting points of the generic affinity...

Select the second of the three starting points of the generic affinity...

Select the third of the three starting points of the generic affinity...

Select the first of the three end points of the generic affinity...

Select the second of the three end points of the generic affinity...

Select the third of the three end points of the generic affinity...

Yet the affinity to all the other data points will be low.

Whether to use a logarithmic gradient for the Kalzium Electron Affinity Gradient feature

Select the triangle that is the image by the affinity of the first triangle...

In other words, it's the affinity that defines those clusters, not the absolute location.

In this data set, the different clusters belong together because they're affinity is similar.

Clearly, the red dots to each other have a high affinity, which means a small quadratic distance.

It is He who created man from water, then gave him consanguinity and affinity. Your Lord is omnipotent.

Specifies whether to use a logarithmic instead of a linear gradient for the Kalzium Electron Affinity Gradient feature

It might just be explaining some of the noise in the affinity matrix that I didn't even dare draw in here.

When Scott Heiferman founded Meetup, he thought it would be used for, you know, train spotters and cat fanciers classic affinity groups.

Now, I have a great affinity for Charles Babbage because his hair is always completely unkempt like this in every single picture.

The second is the affinity with environment. By utilizing natural energy and reducing wastes, we have to make houses which harmonize with ecology.

That's right, I am. But we well know that the reason most of us are here is because of our affinity for disobedience.

You'd begin to see that the other person is somehow close to you. You'll have some affinity towards the others, like they belong to you.

And while it might happen that it's as many as 8, if you adjust you're affinity matrix well, those 2 should correspond with the 2 larger eigenvalues.

An animal? An animal?

An enemy of an enemy isn't necessarily an ally.

Similarly, the affinity of the black data points to each other is very high, which means that the following block diagonal in this matrix will have a very large value.

Consider once again our affinity matrix with prinicple component analysis, which is a standard linear trick, we can re represent this matrix by the most dominant tivectors you'll find there.

The reason being that these aren't clusters defined by a center of data points, but they're clusters define by affinity, which means they're defined by the presence of nearby points.

Cabbages an' 'taters an' onions.

Get an octopus an octopus

An 87, and an 88.