Get Answer: Have Medical Images Question Guide

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Original Question

We have 7 medical images for normal cells and tumor cells. Given the similarity matrix of images as below. Can you cluster these cells into two categories in O(nlogn) time. distance_matrix = [ [0, 38, 17, 28, 88, 59, 13], [38, 0, 52, 49, 83, 91, 59], [17, 52, 0, 46, 34, 77, 80], [28, 49, 46, 0, 5, 53, 62], [88, 83, 34, 5, 0, 43, 33], [59, 91, 77, 53, 43, 0, 27], [13, 59, 80, 62, 33, 27, 0] ] Grading rubric: -2: if complexity is greater than O(nlogn), (if not using Union-Find data structure.) -2: if used library for Union-Find data structure, Implement Union-Find data structure in 10 LOC by yourself. -2: if the clustering is not correct. note: please I only need files with .py extension. hint: in Kruskal’s algorithm, there might be a bunch of disconnected trees in the beginning, but eventually there will be a single MST. You need to merge those disconnected trees using the Union-Find data structure. #!/usr/bin/env python3 # -*- coding: utf-8 -*- #@author: # Function to perform Kruskal’s algorithm for single link k-clustering def greedy_clustering_kruskal(distance_matrix, k): # WRITE YOUR CODE HERE ………………. return list(clusters.values()) # Return the clusters # Use this input distance_matrix = [ [0, 38, 17, 28, 88, 59, 13], [38, 0, 52, 49, 83, 91, 59], [17, 52, 0, 46, 34, 77, 80], [28, 49, 46, 0, 5, 53, 62], [88, 83, 34, 5, 0, 43, 33], [59, 91, 77, 53, 43, 0, 27], [13, 59, 80, 62, 33, 27, 0] ] # Set k=2 for the number of clusters k = 2 clusters = greedy_clustering_kruskal(distance_matrix, k) # Print the resulting clusters print(“Resulting Clusters:”, clusters)

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