In this problem, we have sketched up the code for the K-Mean…
In this problem, we have sketched up the code for the K-Means Clustering algorithm. Please choose options to fill in the blanks. import numpy as np import matplotlib.pyplot as plt def kmeans(X,K,iteration): N = len(X) # Number of data points labels = np.zeros((N,1)) # Cluster labels for each data point centroids = np.zeros((K,X.shape[1])) # Centroid of each cluster # Innitialize: Randomly assign a number C(i) in (1,…,K) to each index i = 1…N for i in range(len(labels)): labels[i] = np.random.randint(0,K) for iteration in range(iteration): # Compute the centroid of cluster K for k in range(K): dp = X[np.where(labels == k)[0]] centroids[k] = _________(1)___________ # Assign observation n to the cluster with closest centroid for n in range(N): distance = np.linalg.norm(X[n]-centroids,axis=1) labels[n] = _________(2)___________ # Compute the distance between each data point and their centroids within_cluster_distance = 0 for m in range(N): within_cluster_distance += _________(3)___________ return within_cluster_distance k_list = [] for i in range(1,10): k_list.append(kmeans(X1,i,10)) x = np.arange(1,10) plt.plot(x,k_list) plt.xlabel(‘K’) plt.ylabel(‘Within Cluster Distance’) plt.show() The format of input $$X$$ is shown below: What should go in the second blank(2)?
Read DetailsSection 11. Hierarchical Clustering (Questions 46-48) In thi…
Section 11. Hierarchical Clustering (Questions 46-48) In this problem, you will run a couple iterations of the hierarchical clustering algorithm in the two-dimensional dataset in the figure below (top subfigure). Using the Dendrograms 1, 2, and 3 for future reference (bottom subfigures), answer the questions below: Using the single-cluster distance, which one of the following dendrograms would be the output of the hierarchical clustering algorithm?
Read DetailsConsider a dataset with points and two classes (red and blu…
Consider a dataset with points and two classes (red and blue) indicated in the figure below. (Note that (0, 0) and (1, 1) are blue points whereas (1, 0) and (0, 1) are red points). Which one of the following equations represents a separating hyperplane for the lifted three-dimensional dataset?
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