Consider the following dataset consisting of the scores of two attributes on each of six objects: Object A1 A2 1.0 1.0 2 2.0 p.5 1.5 1.5 14 2.0 2.0 1.5 4.0 3.5 4.0 We want to group these objects into two dusters using K-means clustering. The distance between objects is the Manhattan distance. After the first iteration of K-means, clusters C1 and C2 have the following objects: C1 = ((1.0, 1.0), (2.0, 0.5)) C2= (15, 1.5), (2.0, 2.0L (1.5, 4.0). (3.5, 4.0]) What are the centroids after one further iteration? Select one: Oa. Centroid 1= [1.5, 0.75) Centroid 2= [2.125, 2.875)

Consider the following dataset consisting of the scores of two attributes on each of six objects: Object A1 A2 1.0 1.0 2 2.0 p.5 1.5 1.5 14 2.0 2.0 1.5 4.0 3.5 4.0 We want to group these objects into two dusters using K-means clustering. The distance between objects is the Manhattan distance. After the first iteration of K-means, clusters C1 and C2 have the following objects: C1 = ((1.0, 1.0), (2.0, 0.5)) C2= (15, 1.5), (2.0, 2.0L (1.5, 4.0). (3.5, 4.0]) What are the centroids after one further iteration? Select one: Oa. Centroid 1= [1.5, 0.75) Centroid 2= [2.125, 2.875)

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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