3. Consider the unlabeled dataset with one feature: {0, 4, 5, 20, 25}. Assume that we want to ob- tain two top-level clusters in this dataset, using bottom-up hierarchical clustering. What will single linkage (minimum distance between members of clusters), complete linkage (maxi- mum distance between members of clusters), and average linkage (average distance between members of clusters) output as the two clusters?

3. Consider the unlabeled dataset with one feature: {0, 4, 5, 20, 25}. Assume that we want to ob- tain two top-level clusters in this dataset, using bottom-up hierarchical clustering. What will single linkage (minimum distance between members of clusters), complete linkage (maxi- mum distance between members of clusters), and average linkage (average distance between members of clusters) output as the two clusters?

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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Consider the data set of points below. Draw the hierarchical clusters obtained by performing agglomerative clustering using single-linkage and complete-linkage. single-linkage complete-linkage
Assume that we use cosine similarity as the similarity measure. In the hierarchical agglomerative clustering (HAC), we need to define a good way to measure the similarity of two clusters. One usual way is to use the group average similarity between documents in two clusters. Formally, for two cluster C; and C, let C= C;U Cj, n = C, we define 1 sim(C,, C) = E s(x, y) Σ n.(n- 1) x.y € C, x =y Where s(x, is the cosine similarity between y) and y. Given a list of clusters C. C2, .... Cm assume that their pairwise similarities are saved in a two dimensional array **", of size m 2. Given three clusters C, C;, and Cr show that there is a way to compute sim(C;U C;, C) in constant time. Note that we ignore the dimensionality in time complexity.
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In Hierarchical clustering, the major difference between the Ward's method and other linkage method is that a. Ward's method uses error sum of squares to measure the loss of information that occurs when cluster are created. b. Ward's method minimizes MAD to minimize the dissimilarity between clusters c. Ward's method uses minimum distance while other linkage methods uses maximum distance between the data points Clear my choice
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