Consider k-means with Euclidean distance function.{(-6, –2), (-5, 2), (-4, –2)} U {(4, 2), (5, –2), (6, 2)}1. Given the two-dimensional dataset S =and k = 2 give the optimal cluster centers that minimize the SSE. Next, choose initializationvalues for the two cluster centers such that k-means never converges to the optimal clustering.2. Proof that there always exists an initialization for which k-means converges to the global op-timum. Use the fact that an iteration of k-means can never increase the SSE (sum of squarederrors, i.e. distances between the cluster center and associated elements).

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Consider k-means with Euclidean distance function. = {(-6,-2), (-5,2), (-4, –2)} U {(4,2), (5, –2), (6, 2)} and k = 2 give the optimal cluster centers that minimize the SSE. Next, choose initialization values for the two cluster centers such that k-means never converges to the optimal clustering. 1. Given the two-dimensional dataset S =

Consider k-means with Euclidean distance function. = {(-6,-2), (-5,2), (-4, –2)} U {(4,2), (5, –2), (6, 2)} and k = 2 give the optimal cluster centers that minimize the SSE. Next, choose initialization values for the two cluster centers such that k-means never converges to the optimal clustering. 1. Given the two-dimensional dataset S =

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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