For the k-means algorithm, it is interesting tonote that by choosing the initial cluster centerscarefully,we may be able to not only speed up theconvergence of the algorithm, but alsoguarantee the qualityof the final clustering. The k-means++algorithm is a variant of k-means, whichchooses the initialcenters as follows. First, it selects one centeruniformly at random from the objects in the dataset.Iteratively, for each object p other than thechosen center, it chooses an object as the newcenter. Thisobject is chosen at random with probabilityproportional to dist(p)2, where dist(p)) is thedistancefrom p) to the closest center that has alreadybeen chosen. The iteration continues until kcenters areselected.Explain why this method will not only speed upthe convergence of the k-means algorithm, butalsoguarantee the quality of the final clusteringresultsjo 9:15

Drawback of normal K-means algorithm: One disadvantage of the K-means rule is that it's sensitive to…

Answered: Explain why this method will not only…

Explain why this method will not only speed up the convergence of the k-means algorithm, but also guarantee the quality of the final clustering results 9.1

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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