Suppose that the data mining task is to cluster points into two clusters, where the points are 2, 3, 4, 10, 11, 12, 20, 25, 30. The distance function is the Euclidian distance. Suppose initially we assign 4 and 12 as the center of each cluster, respectively. Use k-means algorithm to find final clusters and its centroids after convergence.

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
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Suppose that the data mining task is to cluster points into two clusters, where the
points are 2, 3, 4, 10, 11, 12, 20, 25, 30. The distance function is the Euclidian
distance. Suppose initially we assign 4 and 12 as the center of each cluster,
respectively. Use k-means algorithm to find final clusters and its centroids after
convergence.
Transcribed Image Text:Suppose that the data mining task is to cluster points into two clusters, where the points are 2, 3, 4, 10, 11, 12, 20, 25, 30. The distance function is the Euclidian distance. Suppose initially we assign 4 and 12 as the center of each cluster, respectively. Use k-means algorithm to find final clusters and its centroids after convergence.
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