Okay, then we also have this agglomeration cluster Rena algorithm. So for agglomeration clustering. So let's say we have data, and we have all the data points or data objects. So we will calculate all the distances between the data objects and the data points. Then, from other distances, we find the nearest data points or let's say, two data points with nearest distances. Then we group them together.
After we do them, we draw a Venn diagram of something IDs. So let's say A and B has the university students, two we cluster them together. So we have our A and B, then we draw group here and similarity are the distances it's actually our bodies our value here, then D and E we group them together, then the similarity or the distance values, these are all these value here. Then we will calculate all the distances between the data points again very solid data points with the near side distances, can we try to join them together or cluster them together. So, we have this data point here See, then we will try to draw the groups here and similarity or the distances are here. Then we calculate all the distances between all the data points again then we try to find data points to the nearest distances.
Then we try to tie them down we the similarity or distances is around this value here. Then we will continue to calculate all the distances and find the nearest distances and do other clustering until we have one big cluster here. Then we have a dendogram we have something like this. So let's say if we want to have our se t cluster, then we can, let's say, set the cutoff point around here. We can set the cutoff point around here, TV cut off here. We set a cutoff point around here.
Then we will have one cluster here. Then we'll have another cluster here. And then we have one one cluster here are the odd condition or the age above here is all removed. So We only have one cluster, two cluster and then a tree cluster. So, this is the goal moderation clustering. Then every time in the data points we calculate all the distances between the data points.
We saw the distances in these are distance metrics, which can be something like this. So as a air pass these are distance and he had this distance B and D had this distance A and D have the distance as he had the distance A and B have at this distance