1. Using kmeans algorithm and Euclidean distance to cluster the following 8 points into 3 clusters. Using A1 = (2,10), A2 = (2,5), A3 = (8,4), A4 = (5,8), A5 = (7,5), A6 = (6,4), A7 = (1,2) , A8 = (4,9). Consider initial seeds as A1, A4, and A7. Run algorithm for 1 iteration only. At the end of iteration 1, show [40 Points]
The new clusters (i.e. the examples belong to each cluster) The center of the new clusters
Draw 10 10 space and all 8 points and show the clusters after 1st iteration and the new centroids
Without running algorithm again, guess how many more iterations are required to converge. Draw the result of each iteration
2. Using hierarchical clustering algorithms (Single, Complete, Group Average and Distance b/w centroids) and Euclidean distance to cluster the following 8 points into 3 clusters. Using A1 = (2,10), A2 = (2,5), A3 = (8,4), A4 = (5,8), A5 = (7,5), A6 = (6,4), A7 = (1,2) , A8 = (4,9). [40 Points]
3. Review a paper of your choice on Soft Clustering e.g. Fuzzy Kmeans (One Page Summary) [20 Points]
1
