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Hello and welcome back to the course
on Machine Learning.

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Today we're continuing
exploring association rule learning.

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And we're talking about the intuition
behind the model.

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So the cloud model is very, very simple.

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After we have already studied

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the apriori model, it's
kind of like a simplified, version.

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All right. So let's have a look.

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it also talks about the cloud
models, talks about people

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who are bored, also bored.

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So it's kind of like a,
recommender system.

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And similar to what we had in the,

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a, a algorithm.

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here we've got, for instance, movies
and we've got some potential rules.

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So basically exactly the same things.

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If you've got, your,

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movie lists or people,
the movies that people liked, people

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who like movie one hour
or just generally looks like

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if somebody likes movie one,
they're very likely to like movie two,

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if they like movie two,
they're likely to like movie four.

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If they like movie one,
they might like movie three.

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Again, these rules have
will have different, strength.

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But here we are actually going
to be talking about rules per se,

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because the cloud model is different
to the apriori model.

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in the parallel model we came up
with rules towards the end that's output.

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And we based on the left, we could, judge,

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the strength of each rule, whereas here

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we are, going to be talking about sets
and you'll see why just now.

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So here we've got the market basket
optimization.

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Same thing that people who buy burgers
are likely to buy French fries as well.

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People who I buy vegetables,
I like to buy fruits.

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And these are just some potential rules.

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So we're not saying they're, strong

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or some
potential outcomes that we're looking at.

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We're not saying that they're strong
or we're not selecting

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and we're just saying
what could potentially be.

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And then the cloud model is responsible
for actually

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going through all of these combinations
and telling us what we should focus on.

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all right.

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So in the cloud model,
just like in the a priori model,

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we have the support factor.

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So they're producing the apriori model
we had or the algorithm we had support.

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We had confidence
and we had lift in their cloud model.

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We only have support.

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So we only are looking at okay,

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so people who are watching, a certain,

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certain combinations of movies,
how often does this happen?

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And here just bear in mind
that, doesn't mean just one movie.

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And this was the same for a priori.

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It was just easier

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for us to understand the intuition
based on one movie or one product.

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But actually, and I what they stand for
is a set of items or a set of movies.

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So as specifically in the cloud model,
it's doesn't really make sense to,

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look at, you know, an item by itself
because we don't have the,

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confidence and the lift factors.

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We're only looking at support.

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So we're just looking at how frequently
does this set of items, occur.

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So if we just going to

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look at a set of items of, of items
which contains only one item,

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then we just looking at the frequency
and how what

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what is the popularity of movies.
And that is very trivial.

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So we're not going to be looking at that.

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We're going to be aiming
for at least two items in a set.

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And therefore M here
stands for a set of two movies or more.

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And what we're calculating for a support,
we're calculating.

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Okay.

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what is how often does this
set of two movies, let's say,

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interstellar or and Ex Machina,
how often does it

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occur in all of the watch lists,
or what percentage of watch lists

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or what percentage of,

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lists of movies that people liked contain
those two together?

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Not just one of them,
but those two together.

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And let's say if, if hypothetically,

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if 100% of the lists that you had
in a large data set contained both movies

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together, then that would imply that,
you know, anybody who likes

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interstellar, likes Ex Machina, like Ex
Machina likes interstellar,

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and pretty much so if anybody has seen
even one of those movies

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that you need to recommend that movie
or the other one to them,

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if you or if you had like 80% of

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the list of your lists
had those two movies together,

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that basically mean there's a high
likelihood that they come in pairs, right?

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That if somebody liked one of them,

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then they'll like that
same thing for transaction.

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Like if you have,

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chips and burgers and, you know, 75%
of all of your orders, right?

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Then if somebody is just buying, burgers,
then they're likely to,

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then when you recommend chips to them,
there's a 75% chance that they will also

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be interested or will like to buy chips
with their burgers.

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And that's it's a very,
very trivial, approach.

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and that's, that's pretty much it.

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That's all there is to that model.

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It's much faster.

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And the steps involved are,
set a minimum support.

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So you want to set your, support level
at which you want to,

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only after below,
which you want to disregard anything.

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then you take all the subsets
and transactions having higher support

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and mind and support and then you sort
this subset in decreasing support.

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And basically at the top
you will have the most, the strongest,

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combinations of items,
which you should look at,

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maybe, you know, you'll look at the top
ten or to file or something like that.

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So that's pretty much it.

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That's all the A club model is.

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And as you can see, it's much easier
to understand after you already know,

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a bit about the apriori.

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All right. Hope you enjoyed this tutorial.

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And if we go to learned to

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implement this in practice
and I'll see you here next time.

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And till then happy analyzing.