Once it's shrank, it has a greater effect to outliers than to the normal points.

Because of the outlier, even you have a chance to find outliers pretty

far away from this and you take it as a rate, representative point.

Because it is far away from the center with this factor alpha,

it will shrink dramatically towards the center, okay?

That's why the outlier, effect will be minimized, okay?

So, you probably can see, with this, map, transformation, okay?

It improves the efficiency and, and it moreover improved the effectiveness.

Actually the paper also taking a set of

the points you know form into different shapes.

You'll probably see if you use a typical like a k-mean space approach

you're going to find the cluster in the center.

You'll find a very ugly shape.

And, these kind of clusters we're,

actually getting into you know, these ugly shaped cluster,

we're painting those connected objects into different clusters, different colors.