ANSWER WITHOUT USE OF LIBRARIES.

1 Clustering Algorithm In this assignment you will implement a simplified won of k-means algorithm taxing Python to cluster toy dataset comprising five data points into two clusters. It is not necessary that you should be we of clustering algorithm to complete this task. The stope wolved to build the clustering algorithm in this task is provided below. You are required to implement the described steps in Python to build the algoritam. You can complete this task by sing functions and for loops in Python More specifically, comalet the following dataset comprising five data pelnita (2-timentul) {(0,0).(1.0), (1,1),(0,1).(-10) The steps involved in developing the clustering algorithm are as follows: 1. Step 1: Choose the amber of controids (data points) required to create clusters. Since we need to create tweelisters, we select two centroids. Specifically in this gent, we will choose (1,0) and (1.1). We will call and cu or two initial clusters with centrode (1.0) and (1.1) 2. A clustering algorithm sully involves a set of iterations to go through the data poltits several times before creating the clusters. In this simple task, we will use only two iterations to go through the data points. Remember one iteration involves processing each of the data points once. You are required to do the following in the first and second iteration as described below: 3. Ist Iteration compiute distance between each data point in the dataset and the centres in both clusters which we have initialised in Step 1 add the data point to the cluster that has minim distance from a given centroid in the claster. To compute the distance detine a function that takes two points as arguments and computes the distance between the points asing the equation: dir)+(-) at the end of first iteration, you will have a set of points chisted in each of the clusters based on the distance between controld. The total set of points in both the clusterx will be five Compute the mean of the set of points in each chester. To do so, tem a function that take in a set of points and returns the mean of the set of points, pused as arguments to the function teinitialise the clusters and with the mean values 42nd leration repeat the same stes that you performed during the first iteration to go through each data point to compute the distance between each data point and the new munan valien obtained in the first iteration at the end of 2 iterations, you will have once again have two chiesters with a set of points clustered together. The total set of points in both the clusters will remain tive . At the end of second iteration, when you compute the moun, you will notice that the mean values are not changed. This means that you live stressfully clustered the five data points into two chiesters 1 Clustering Algorithm In this assignment you will implement a simplified won of k-means algorithm taxing Python to cluster toy dataset comprising five data points into two clusters. It is not necessary that you should be we of clustering algorithm to complete this task. The stope wolved to build the clustering algorithm in this task is provided below. You are required to implement the described steps in Python to build the algoritam. You can complete this task by sing functions and for loops in Python More specifically, comalet the following dataset comprising five data pelnita (2-timentul) {(0,0).(1.0), (1,1),(0,1).(-10) The steps involved in developing the clustering algorithm are as follows: 1. Step 1: Choose the amber of controids (data points) required to create clusters. Since we need to create tweelisters, we select two centroids. Specifically in this gent, we will choose (1,0) and (1.1). We will call and cu or two initial clusters with centrode (1.0) and (1.1) 2. A clustering algorithm sully involves a set of iterations to go through the data poltits several times before creating the clusters. In this simple task, we will use only two iterations to go through the data points. Remember one iteration involves processing each of the data points once. You are required to do the following in the first and second iteration as described below: 3. Ist Iteration compiute distance between each data point in the dataset and the centres in both clusters which we have initialised in Step 1 add the data point to the cluster that has minim distance from a given centroid in the claster. To compute the distance detine a function that takes two points as arguments and computes the distance between the points asing the equation: dir)+(-) at the end of first iteration, you will have a set of points chisted in each of the clusters based on the distance between controld. The total set of points in both the clusterx will be five Compute the mean of the set of points in each chester. To do so, tem a function that take in a set of points and returns the mean of the set of points, pused as arguments to the function teinitialise the clusters and with the mean values 42nd leration repeat the same stes that you performed during the first iteration to go through each data point to compute the distance between each data point and the new munan valien obtained in the first iteration at the end of 2 iterations, you will have once again have two chiesters with a set of points clustered together. The total set of points in both the clusters will remain tive . At the end of second iteration, when you compute the moun, you will notice that the mean values are not changed. This means that you live stressfully clustered the five data points into two chiesters