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.
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
Section: Chapter Questions
Problem 1PE
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- 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
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