Given are the points A = (3,4), B = (4,4), C = (4,3), D = (-3,6), E = (-4,-3), F = (-3,-3). Starting from initial clusters Cluster1 = {A} which contains only the point A and Cluster2 = {D} which contains only the point D, run the K-means clustering algorithm and report the final clusters. Use L1 distance as the distance between points which is given by d ((x1, y1), (x2, y2)) = | x1 – x2 | + | y1 – y2 |. Draw the points on a 2-D grid and check if the clusters make sense. You may stop the clustering process if it found that two iterations have the same clusters, otherwise, at least 4 iterations are required. Given are the 1-dimensional points A = 2, B = 3, C = 4, D = 9, E = 10, F = 11. Compute complete-linkage hierarchical clustering using d (x, y) = |x – y| as the distance between points. And also draw a dendrogram of it.
Given are the points A = (3,4), B = (4,4), C = (4,3), D = (-3,6), E = (-4,-3), F = (-3,-3). Starting from initial clusters Cluster1 = {A} which contains only the point A and Cluster2 = {D} which contains only the point D, run the K-means clustering
Given are the 1-dimensional points A = 2, B = 3, C = 4, D = 9, E = 10, F = 11. Compute complete-linkage hierarchical clustering using d (x, y) = |x – y| as the distance between points. And also draw a dendrogram of it.
Trending now
This is a popular solution!
Step by step
Solved in 2 steps with 1 images
