How in this session, we will discuss about the next unsupervised learning algorithm that the clustering
algorithm named SCAN.
Whatever drawbacks we have in case of humans would not be best in case of Busca.
So let us discuss about the scam.
So in case of gaming's clustering, we needed the number of clusters and we used to create very good
clusters in case of gaming's clusters.
And we would fix two separate points based on which the clusters would be created.
Now, because of those Troyes, the clusters would meet radically initial.
But in case of the rescan, we will not be declaring how many cluster's people to babies can or although
they take the number of clusters, so for this they become important.
We will need to barometer's the first family there is Epsilon and which is the neighborhood size.
And the second parameter is the minimum number of point, which is required to form a cluster.
Now, in case of a baby scan, it will also detect the number of clusters and it also does not make
assumptions of those vertical clusters, which was happening in case of gaming's in babies can it can
be used for anomaly detection and it does not get impacted by the outliers because the clusters will
be formed based on the minimum number of points, which we will be declaring.
So if there are any outliers, then based on these minimum points, we can actually figure that out,
those outliers, and not consider them for those clustered.
So for this, we will go to the same website where we can actually visualize the clustering.
So this is the website for visualizing the clustering.
So here we can simply select the type of data which people want.
So let's try the smiley face again.
Now in this smiley face.
We will have to provide the absolute value and the minimum point's value, so minimum point's value
will actually define how many points are required to actually form clusters.
So for this, the minimum point is stated.
As for the absolute value is given as one, which means that how many neba we want to have, the higher
the value of Epsilon, the more points will be considered in case of one creating the neighborhood.
So let's run this.
Now, I want to begin this, it will randomly start to the starting point and start creating the clusters.
Here you can see how easily the scan was able to find out unflawed photo of the clusters which will
present.
Now, let us try to look at more details, that is try to run another visualisations so we go to restart
and this time we select the Pembridge Smiley.
And let's decrease the Absolon value.
So now the Absolon value is zero point six zero.
So here you can see it is currently checking for different point.
So it started checking these points, which are presently in the middle, but because it was not able
to find four point one particular place, so it could not really create clusters of of these points.
So let me increase the epsilon value for this one.
Now, you can see that because the absolute value was low, smaller neighborhood clusters were created
because there were more number of clusters created.
Now, if I will increase the value to let us say it for Epsilon.
Now, let's run this.
And let us keep the minimum number of points for clustered as three instead of.
With higher value of epsilon, you can see the cluster making process is also Plaistow.
And you will see that it is checking into each and every point for the cluster creation, but wherever
it will find at least three point, there will be it will create a cluster.
But you can see it has detected all of the point as outliers, so let us try one more scenario.
Now, let us have a look at the exemptions, so as we saw individualisation similarly here, you can
see that it is able to take the clusters, even though they are not spherical in nature.
It will detect the clusters in whatever shape.
They are fresh and it can detect different patterns which are present in the data.
And it will not try to create vertical clusters.
And it will also isolate the outliers.
So it will keep the outliers aside so that we will know that these are actually not a part of the ditzen.
So here is a comparison of Baby Scammon Kamins, so baby scan is able to detect these dooring separately
like means try to create very few clusters.
Same thing applied for these two harpoons.
And here again, a baby scan was able to find both the different classes.
So this is different.
And so they're different clusters.
Was able to group these together while here in gaming's.
It just right through a cluster and spherical vs.
Here you can see that it has created three different clusters in Wisconsin canings, which are spherical
clusters.
So it has evolved as good as K means algorithm.
I'm here you can see the most important example, which is uniform distribution.
So the scan does not impose clusters if there are no different clusters in the data.
So here there are actually no clusters and this is able to detect that thing while gaming's tries to
create clusters, no matter the president or not.
Now, to think the quality of the posters which have created we have this scoring metric which is scored
for you, it is also called Sillett Index.
If the index value is high, it means that the object is very much the way it's all cluster and poorly
matched to the neighboring cluster.
So Senate coefficient is calculated using the mean distance and the mean nearest cluster of distance.
That is a and so those select corporations will be defined as s.a given by by minus E divided by a maximum
of NBA.
So here is the average dissimilarity of I object to all of the objects in the same cluster.
So it compares the inverse cluster of cluster distance V VI compares the distance of a particular weight
with the of all objects in the closest cluster.
So this is what's called a gift.
So we want this sentence to be closer to one.
If the school of school is close to one, it means that the clusters are well formed.
And if the center of school is minus one, it means that the clusters are actually placed in different
clusters.
So instead of being placed in the cluster, a point has been missed class of misplaced in a different
cluster.
And when the Szilard index value is zero, it means that the clusters are kind of overlapping in nature.
So this is about the range of the value.
That is, if citified is close to one, the sample is well clustered.
If civic value is zero, could be assigned to one of the cluster closest to it.
I'm topolice equally far away from both the clusters that this means that it indicates overlapping clusters
and absolute value is minus one.
Then it means that the sample is misclassified.
So one is the perfect score and we want the solid score to be as close to one as possible.
So this is about six score.
Next, we will implement the babies can include them and so that you will get a picture and be able
to compare different clustering algorithms that have had a good game means and they'll be scanned.
And as we already told, a DV scan is used when we don't have any spherical clusters and ketamine's
works better than we have spherical clusters and we actually know the number of clusters.
In case we don't know what is the number of clusters, then we can easily use the rescan by providing
the epsilon and minimum number of point value.
The scam is used when we have certain outliers and then we want to isolate the outliers and I want to
consider the other players in this condition.
And regarding how to head up clustering, so hierarchical clustering would be helpful when we have a
smaller dataset.
So this is one restriction which hierarchical data clustering has.
Otherwise hierarchical clustering is very nice clustering method because it does not change the type
of cluster with each and every one like gaming's.
I'm the rescan, so it gives the same cluster everytime had the clustering.
So this is of one difference between these three clustering and qualities.
So next we will see the implementation.
So you will get a clearer picture of the same.