# Create a Program to Implement K Means Clustering in Python Assignment Solution.

## Instructions

Objective
Write a python assignment program to implement K means clustering.

## Requirements and Specifications

Source Code

```import numpy as np from scipy.spatial.distance import cdist import matplotlib.pyplot as plt data = np.loadtxt('data_points.txt', delimiter=',') data.shape ### Define number of clusters, tolerance and maximum number of iterations K = 4 tol = 1E-6 max_iters = 15 ### Pick centroids centroids = data[np.random.choice(data.shape[0], K),:] print(centroids) ### Begin with K-Means Clustering Algorithm # Define a numpy array to label each point labels = np.zeros((data.shape[0],1)) # Define initial error err = 1E10 iters = 0 while err > tol and iters < max_iters: # Calculate distances to centroids distances = cdist(data, centroids) # Pick the minimum distance index idx = np.argmin(distances, axis = 1) # Now, update centroids old_centroids = centroids.copy() for k in range(K): # Calculate the new value of centroid k idxs_ = np.where(idx == k)[0] centroid = np.mean(data[idxs_], axis = 0) centroids[k] = centroid labels[idxs_] = k # Now, compute error err = np.max(np.abs(old_centroids - centroids)) iters = iters + 1 # Print print("Iteration {0}, error = {1:.8f}".format(iters, err)) ### Plot centroids and points plt.figure() for k in range(K): idxs_ = np.where(labels == k)[0] p = data[idxs_,:] plt.scatter(p[:,0], p[:,1], label = f"K = {k}", marker="+") # Plot centroids plt.scatter(centroids[:,0], centroids[:,1], color='black', marker = "x")```