step 1 has already been complete.

1 Clustering Algorithm In this assignment you will implement simplified version of sporithm Python to cluster a toy dataset comprising five data points into two clusters. It is not necessary that you should be aware of clustering algoritlum to complete this task. The steps involved to build the clustering algorithm in this task is provided below. You are required to implement the described stepe in Python to build the algorithm. You can complete this tak by sing functions and for koop in Python More specifically, comssler the following dataset comprising five data points (3-dimensional) {(0,0).(1.0).(1.1).(0.1).(-1,0)) The steps involved in developing the clustering algorithm are Sottoms 1. Step 1: Choose the member of controids (data points required to create chusters. Since we need to create two clusters, we select two centro. Specifically in the ment, we will choose = (1.0) and = (1.1). We will call and as to initial clusters with centrode (1.0) and (1.1) 2. A clustering algorithm nully involves a set of iterations to pop through the data pointe eral times before creating the clusters. In this simple task, we will be only two iterations to go through the data point. Remember one iteration involves pecing each of the data points obce. You are required to do the following in the first and second iteration as described below 3. Ist Iteration . compute distance between each data point in the dataset at the centroids in both clusters which we live initialised in Step 1 add the data point to the chuster that uns minimum distance from a given centred in the cluster. To compute the distance define a function that takes two points as a guest and computer the distance between the points in the equatic - Viens +(2-1) . It the end of first iteration, you will have a set of points clustered in each of the clusters based on the distance between centroid. The total set of points in both the clusters will be five . Compute the mean of the set of points in each cluster. To do so define a function that tales in a set of points and returns the men of the set of produits passed as arguments to the function . reinitalise the clusters, and on with the men altes. 4. 2nd Iteration . repeat the same steps that you performed during the first itemtion to go through each data point to compute the distance between each data point and the new mean values obtained in the first iteration at the end of 2nd iterations, you will love once i have two clusters with a stof point clustered together. The total set of points in both the destens will remain five . At the end of second iteration, when you compute the mean you will notice that the mean values have not changed. This means that you are fully clustered the five dalam into the cute 1 Clustering Algorithm In this assignment you will implement simplified version of sporithm Python to cluster a toy dataset comprising five data points into two clusters. It is not necessary that you should be aware of clustering algoritlum to complete this task. The steps involved to build the clustering algorithm in this task is provided below. You are required to implement the described stepe in Python to build the algorithm. You can complete this tak by sing functions and for koop in Python More specifically, comssler the following dataset comprising five data points (3-dimensional) {(0,0).(1.0).(1.1).(0.1).(-1,0)) The steps involved in developing the clustering algorithm are Sottoms 1. Step 1: Choose the member of controids (data points required to create chusters. Since we need to create two clusters, we select two centro. Specifically in the ment, we will choose = (1.0) and = (1.1). We will call and as to initial clusters with centrode (1.0) and (1.1) 2. A clustering algorithm nully involves a set of iterations to pop through the data pointe eral times before creating the clusters. In this simple task, we will be only two iterations to go through the data point. Remember one iteration involves pecing each of the data points obce. You are required to do the following in the first and second iteration as described below 3. Ist Iteration . compute distance between each data point in the dataset at the centroids in both clusters which we live initialised in Step 1 add the data point to the chuster that uns minimum distance from a given centred in the cluster. To compute the distance define a function that takes two points as a guest and computer the distance between the points in the equatic - Viens +(2-1) . It the end of first iteration, you will have a set of points clustered in each of the clusters based on the distance between centroid. The total set of points in both the clusters will be five . Compute the mean of the set of points in each cluster. To do so define a function that tales in a set of points and returns the men of the set of produits passed as arguments to the function . reinitalise the clusters, and on with the men altes. 4. 2nd Iteration . repeat the same steps that you performed during the first itemtion to go through each data point to compute the distance between each data point and the new mean values obtained in the first iteration at the end of 2nd iterations, you will love once i have two clusters with a stof point clustered together. The total set of points in both the destens will remain five . At the end of second iteration, when you compute the mean you will notice that the mean values have not changed. This means that you are fully clustered the five dalam into the cute