In this video, I'd like to start to talk about clustering.
This will be exciting because this
is our first unsupervised learning algorithm
where we learn from unlabeled data
instead of the label data.
So, what is unsupervised learning?
I briefly talked about unsupervised
learning at the beginning of
the class but it's useful
to contrast it with supervised learning. So, here's
a typical supervisory problem where
we are given a label training sets
and the goal is to find
the decision boundary that separates the
positive label examples and the negative label examples.
The supervised learning problem in
this case is given a
set of labels to fit a hypothesis to it.
In contrast, in the unsupervised
learning problem, we're given
data that does not have
any labels associated with it.
So we're given data that looks
like this, here's a set
of points and then no labels.
And so our training set is written
just x1, x2 and
so on up to x(m)
and we don't get any labels y.
And that's why the points plotted
up on the figure don't have
any labels on them.
So in unsupervised learning, what
we do is, we give this sort of
unlabeled training set to
an algorithm and we just
ask the algorithm: find some
structure in the data for us.
Given this data set, one
type of structure we might
have an algorithm find, is that
it looks like this data set has
points grouped into two
separate clusters and so
an algorithm that finds that
clusters like the ones I just
circled, is called a clustering
algorithm.
And this will be our first type of
unsupervised learning, although there
will be other types of unsupervised
learning algorithms that we'll talk
about later that finds other
types of structure or other
types of patterns in the data other than clusters.
We'll talk about this afterwards, we will talk about clustering.
So what is clustering good for?
Early in this class I had already mentioned a few applications.
One is market segmentation, where
you may have a database of
customers and want to group
them into different market segments.
So, you can sell to
them separately or serve your different market segments better.
Social network analysis, there are
actually, you know, groups that have done
this, things like looking at a
group of people, social networks,
so things like Facebook, Google plus
or maybe information about who
are the people that you
email the most frequently and who
are the people that they email
the most frequently, and
to find coherent groups of people.
So, this would be another maybe
clustering algorithm where, you know, you'd want
to find who other coherent groups of friends in a social network.
Here's something that one of my
friends actually worked on, which is,
use clustering to organize compute
clusters or to organize data
centers better because, if you
know which computers in the
data center are in the cluster there tend to work together.
You can use that to reorganize
your resources and how you
lay out its network and
how design your data center and communications.
And lastly something that, actually
another thing I worked on, using
clustering algorithms to understand
galaxy formation and using
that to understand how, to
understand astronomical detail.
So, that's clustering which
is our first example of
an unsupervised learning algorithm.
In the next video, we'll start to
talk about a specific clustering algorithm.