Given the following eight points point 1 point 2 point 3 point 4 point 5 point 6 point 7 point 8 (4,1) (7,6) (6,2) (2,4) (5,3) (1,7) (8,5) (3,8) cluster them using the k-means algorithm (all distances are Euclidean) until convergence, i.e. until there is no change in clusters. Assume the parameter k = 3 and the initial cluster centroids are point 1, point 2, and point 3. What is the resulting clustering? O a. {1, 2, 4), (5, 6, 8}, {3,7} O b. {1, 4), (6, 8), (2, 3, 5, 7} O c. none of the proposed answers O d. {4}, {1, 6, 8}, {2, 3, 5, 7} O e. {1}, {4, 6}, {2, 3, 5, 7, 8) O f. {1, 4, 6), (3, 8), (2, 5, 7)

Given the following eight points point 1 point 2 point 3 point 4 point 5 point 6 point 7 point 8 (4,1) (7,6) (6,2) (2,4) (5,3) (1,7) (8,5) (3,8) cluster them using the k-means algorithm (all distances are Euclidean) until convergence, i.e. until there is no change in clusters. Assume the parameter k = 3 and the initial cluster centroids are point 1, point 2, and point 3. What is the resulting clustering? O a. {1, 2, 4), (5, 6, 8}, {3,7} O b. {1, 4), (6, 8), (2, 3, 5, 7} O c. none of the proposed answers O d. {4}, {1, 6, 8}, {2, 3, 5, 7} O e. {1}, {4, 6}, {2, 3, 5, 7, 8) O f. {1, 4, 6), (3, 8), (2, 5, 7)

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Using MST for Clustering With the following reasoning: A tree is acyclic, and every edge of a tree is a bridge, hence a minimum spanning tree of a weighted graph may be utilised for clustering. As a result, splitting a graph G's MST into two clusters and continuing in this way results in clusters. To get k clusters, we must eliminate the k 1 strongest edges from the graph's MST. Since it is challenging to predict the value of k, a threshold of may be used to generate the clusters by removing from the MST those edges with weights greater than. This algorithm's further phases can now be defined.1. Input: A weighted graph G = (V, E, w), threshold τ . 2. Output: C = {C1,C2,...,Ck }, k clusters of G.3. Find MST T of G.4. Remove all edges from G that have a weight larger than τ .5. Find the components of GN which are the clusters of G Make Python Implementation
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