Okay, we also have these are k means clustering. So for cluster when I say we have data, and then we have all these data points or data objects. So these are data points and data objects, we try to group these data points and data objects into groups. In clustering we call the groups as our cluster. So, in clustering we say that we cluster the data into clusters. So, for K means algorithm, so let's say we have a data set, and then we have all these data points here.

Then, we try to specify the k. So, let's say we specify that k is two. So, we select two data points as the US centroids then we try to calculate all the distances between the data points and the centroids. from other distances, we will cluster the data points into two groups here are two cluster here. After we had these two cluster, we will use the mean, we will calculate the mean or the s and y are the data points. So, we calculate the mean and then we will get a new st right here. So, we'll get to new centroid then we try to find all the distances are between all the data points and data objects between the data points and data objects and we try to find distances between the new centroids and data points and data objects.

Then from all these distances, we try to cluster the data points or the data objects into two groups. Then after we cluster the data points and data objects into two groups, then we try to calculate a new centroid again then we repeat the process, we find all the distances and then we try to cross the other data points based on the new centroids. We will repeat this process until we get a cluster that does not have any more changes. So, we have to group here because our K v specifies to so EV specify that k as I say 30 we will have 30 groups or 30 cluster So, this SR k means clustering. I'll go