Question 7 Consider seven points in 1-D, the one-dimensional space. Suppose a partitioning into two clusters C1 and C2 has been obtained by a k-medoids method (i.e., k=2). Let m1 and m2 be the representative objects of C1 and C2, respectively, m1 = 23 and m2 = 28. Cluster C1 has been assigned the (non-representative) points %3D P1 = 40 P2 = 12 and cluster C2 the points P3 = 6 P4 = 9 P5 = 34 Using the Manhattan distance (i.e., the absolute value of the difference between two points) as the dissimilarity measure, calculate the absolute error E of the given partitioning.
Question 7 Consider seven points in 1-D, the one-dimensional space. Suppose a partitioning into two clusters C1 and C2 has been obtained by a k-medoids method (i.e., k=2). Let m1 and m2 be the representative objects of C1 and C2, respectively, m1 = 23 and m2 = 28. Cluster C1 has been assigned the (non-representative) points %3D P1 = 40 P2 = 12 and cluster C2 the points P3 = 6 P4 = 9 P5 = 34 Using the Manhattan distance (i.e., the absolute value of the difference between two points) as the dissimilarity measure, calculate the absolute error E of the given partitioning.
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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Transcribed Image Text:Question 7
Consider seven points in 1-D, the one-dimensional space.
Suppose a partitioning into two clusters C1 and C2 has
been obtained by a k-medoids method (i.e., k=2). Let m1
and m2 be the representative objects of C1 and C2,
respectively, m1 = 23 and m2 = 28. Cluster C, has been
assigned the (non-representative) points
P1 = 40
P2 = 12
and cluster C2 the points
P3 = 6
P4 = 9
P5 = 34
Using the Manhattan distance (i.e., the absolute value of
the difference between two points) as the dissimilarity
measure, calculate the absolute error E of the given
partitioning.

Transcribed Image Text:Question 8
Suppose that the k-medoids algorithm is being used to
obtain a partitioning of a dataset of 2-dimensional points.
Consider two representative points m1 and m2
m1 = (14, 49)
m2 = (36, 22).
%3D
%3D
Suppose that p = (6, 53) is currently assigned to the
cluster represented by m1. If m2 were replaced by a
random non-representative point r,
= (41, 19),
would p be assigned to the cluster represented by r, or
would p remain assigned to cluster represented by m1?
Assume the Euclidean distance is used as the dissimilarity
measure.
Enter 1 is the answer is p would be assigned to the cluster
represented by r.
Enter O if the answer is p would remain assigned to cluster
represented by m1.
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