Electron Affinity

Generic Affinity

Generic affinity of this object

I feel a strong affinity for her.

This case has an affinity with that one.

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.

I had an affinity for motercycles when I was young.

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.

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

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.

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

But they can't avoid low birth rate which will come after this period, and the labor cost will hit the roof and reach the maturation period.

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

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

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.

Then just like what's happening in Japan right now, just putting more time into work won't help them in the maturation period in order to survive in this highly competitive world.

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.

It was kind of a nasty way to get maturation, but we got it, because we all understood it, and for the first time that I could remember, there were mass protests against this corruption.

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.

In fact, I'd say there's a great story in the maturation cycle of evolving species, where they first are young and grabby they multiply and take all the territory they can, bump off their competitors, act like capitalists.

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.

Now the key element of spectral clustering is called the affinity martrix, which is a 9 by 9 matrix in this case, where each data point gets graphed realtive to each other data point.