Hello and welcome back to the course on Machine Learning.
Today we're continuing exploring association rule learning.
And we're talking about the intuition behind the model.
So the cloud model is very, very simple.
After we have already studied
the apriori model, it's kind of like a simplified, version.
All right. So let's have a look.
it also talks about the cloud models, talks about people
who are bored, also bored.
So it's kind of like a, recommender system.
And similar to what we had in the,
a, a algorithm.
here we've got, for instance, movies and we've got some potential rules.
So basically exactly the same things.
If you've got, your,
movie lists or people, the movies that people liked, people
who like movie one hour or just generally looks like
if somebody likes movie one, they're very likely to like movie two,
if they like movie two, they're likely to like movie four.
If they like movie one, they might like movie three.
Again, these rules have will have different, strength.
But here we are actually going to be talking about rules per se,
because the cloud model is different to the apriori model.
in the parallel model we came up with rules towards the end that's output.
And we based on the left, we could, judge,
the strength of each rule, whereas here
we are, going to be talking about sets and you'll see why just now.
So here we've got the market basket optimization.
Same thing that people who buy burgers are likely to buy French fries as well.
People who I buy vegetables, I like to buy fruits.
And these are just some potential rules.
So we're not saying they're, strong
or some potential outcomes that we're looking at.
We're not saying that they're strong or we're not selecting
and we're just saying what could potentially be.
And then the cloud model is responsible for actually
going through all of these combinations and telling us what we should focus on.
all right.
So in the cloud model, just like in the a priori model,
we have the support factor.
So they're producing the apriori model we had or the algorithm we had support.
We had confidence and we had lift in their cloud model.
We only have support.
So we only are looking at okay,
so people who are watching, a certain,
certain combinations of movies, how often does this happen?
And here just bear in mind that, doesn't mean just one movie.
And this was the same for a priori.
It was just easier
for us to understand the intuition based on one movie or one product.
But actually, and I what they stand for is a set of items or a set of movies.
So as specifically in the cloud model, it's doesn't really make sense to,
look at, you know, an item by itself because we don't have the,
confidence and the lift factors.
We're only looking at support.
So we're just looking at how frequently does this set of items, occur.
So if we just going to
look at a set of items of, of items which contains only one item,
then we just looking at the frequency and how what
what is the popularity of movies. And that is very trivial.
So we're not going to be looking at that.
We're going to be aiming for at least two items in a set.
And therefore M here stands for a set of two movies or more.
And what we're calculating for a support, we're calculating.
Okay.
what is how often does this set of two movies, let's say,
interstellar or and Ex Machina, how often does it
occur in all of the watch lists, or what percentage of watch lists
or what percentage of,
lists of movies that people liked contain those two together?
Not just one of them, but those two together.
And let's say if, if hypothetically,
if 100% of the lists that you had in a large data set contained both movies
together, then that would imply that, you know, anybody who likes
interstellar, likes Ex Machina, like Ex Machina likes interstellar,
and pretty much so if anybody has seen even one of those movies
that you need to recommend that movie or the other one to them,
if you or if you had like 80% of
the list of your lists had those two movies together,
that basically mean there's a high likelihood that they come in pairs, right?
That if somebody liked one of them,
then they'll like that same thing for transaction.
Like if you have,
chips and burgers and, you know, 75% of all of your orders, right?
Then if somebody is just buying, burgers, then they're likely to,
then when you recommend chips to them, there's a 75% chance that they will also
be interested or will like to buy chips with their burgers.
And that's it's a very, very trivial, approach.
and that's, that's pretty much it.
That's all there is to that model.
It's much faster.
And the steps involved are, set a minimum support.
So you want to set your, support level at which you want to,
only after below, which you want to disregard anything.
then you take all the subsets and transactions having higher support
and mind and support and then you sort this subset in decreasing support.
And basically at the top you will have the most, the strongest,
combinations of items, which you should look at,
maybe, you know, you'll look at the top ten or to file or something like that.
So that's pretty much it.
That's all the A club model is.
And as you can see, it's much easier to understand after you already know,
a bit about the apriori.
All right. Hope you enjoyed this tutorial.
And if we go to learned to
implement this in practice and I'll see you here next time.
And till then happy analyzing.