Cluster the following eight points (with (x, y) representing locations) into three clusters:   A1(2, 10), A2(2, 5), A3(8, 4), A4(5, 8), A5(7, 5), A6(6, 4), A7(1, 2), A8(4, 9)   Initial cluster centers are: A1(2, 10), A4(5, 8) and A7(1, 2). The distance function (called Manhattan distance) between two points a = (x1, y1) and b = (x2, y2) is defined as   D(a, b) = |x2 – x1| + |y2 – y1|   Use K-Means Algorithm to find the three cluster centers after the second iteration.

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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  1. Cluster the following eight points (with (x, y) representing locations) into three clusters:

 

A1(2, 10), A2(2, 5), A3(8, 4), A4(5, 8), A5(7, 5), A6(6, 4), A7(1, 2), A8(4, 9)

 

Initial cluster centers are: A1(2, 10), A4(5, 8) and A7(1, 2).

The distance function (called Manhattan distance) between two points a = (x1, y1) and b = (x2, y2) is defined as

 

D(a, b) = |x2 – x1| + |y2 – y1|

 

Use K-Means Algorithm to find the three cluster centers after the second iteration.

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