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Okay, well we've talked quite exhaustively about this notion of clustering for
the sake of doing document retrieval, but there are lots,
and lots of other examples where clustering is useful, and
I wanna take some time just to describe a few of them.
So one application is for image search.
So imagine you're going and you're searching,
you go on Google Image search and you type in the word ocean.
Well it would be really helpful if we could structure all the images we have by
some set of categories like ocean, pink flower, dog, sunset, clouds.
So clustering is very helpful for doing structured search.
Another very different application is maybe we wanna group patients by
their medical condition.
So here a goal might be to better
characterize subpopulations as well as different diseases.
So as an example, we can look at a whole bunch of patients that have seizures.
So these three brains represent three different patients, and
they have different recording setups that are measuring their seizure activity.
And so for each of these patients we get a collection of recordings
of different seizures that they exhibit over time.
So each one of these colored squares represents
a different recording of a seizure.
And between these different patients there might be
similar types of seizures that appear in these different patients.
And so what we can do is we can take all of these seizure recordings from these
three different patients and think about clustering them.
And if we identify different types of seizures in this way,
this can allow us to better treat the types of patient that
we're observing based on understanding what types seizures they exhibit.
Well another application is thinking about doing product recommendation on Amazon.
So, for example, on Amazon there are a lot of third parties that come and
they post some product to be sold.
And they provide a label of what that product is.
So, for example, maybe a person wants to sell a crib and
they label the crib, fairly reasonably, as being a furniture item.
So maybe we get posted under the furniture category.
But, if instead, we look at who purchases this item?
And we look at their purchase history, and
we look at other people with similar purchase histories, so
maybe the person who purchased this item also purchased baby car seat, well
then maybe what we can do is maybe we can infer that a better label for this crib,
which had been labeled furniture is really to have labeled it as a baby product.
So in addition to discovering groups of products that are related, that have.
Based on purchase histories of these items we can also
use that to discover groups of related users on Amazon.
And that can be used for targeting products to those users.
And finally we can think about structuring web search results.
So, for example,
search terms can have multiple meanings like the word "cardinal".
If I type this in to Google, maybe I mean I want an article about a cardinal,
the bird, maybe about the baseball team, or about a cardinal, a religious figure.
So if we can structure out articles based on their content,
using the same types of ideas we've talked about in this module,
then I can improve my search results that I provide to people.
And the list of applications goes on and on.
Another one that's quite interesting is thinking about
collections of neighborhoods and
there are a few applications where you want to discover similar neighborhoods.
One is if we wanna estimate the price of a house at a very small local
regional level.
So in this case, it challenges the fact that we only have a few, or
very often, no house sale observations within a very small neighborhood.
So if we wanna estimate the value of the house in that neighborhood
at a point in time, it's very hard to do that
because we have no other houses to base our estimate off of in that neighborhood.
However, if we can discover other neighborhoods that have
similar types of house dynamics, house price dynamics, then
we can come up with a good estimate of the house in the neighborhood with few or
no sales by leveraging information from this other neighborhood
that was discovered to be related to the current neighborhood.
So, the idea is to discover clusters of neighborhoods, and
then within those clusters we can share information like these house sales
informations to form better estimates.
So, this is the solution that I'm describing here, is to cluster regions
with similar trends, and then share information within a cluster.
And the same idea of discovering related regions can be used for helping
to forecast violent crimes, to better task police forces to different regions.
So again once we discover different neighborhoods that have very
similar crime dynamics, we can form better predictions of the rates
of violent crimes in those neighborhoods and
then use that information to task police to those regions.
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