ML_WEEK7_QUIZ

ML_WEEK7_QUIZ

University

15 Qs

quiz-placeholder

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ML_WEEK7_QUIZ

ML_WEEK7_QUIZ

Assessment

Quiz

Information Technology (IT)

University

Hard

Created by

Kenny KenWJ

FREE Resource

15 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is K-Means clustering primarily used for?

Classification of labeled data

Clustering of unlabeled data

Feature selection

Anomaly detection

2.

MULTIPLE SELECT QUESTION

30 sec • 1 pt

What are common criteria for K-Means to converge? (Select all that apply)

Maximum number of iterations reached

No points change their assigned cluster

Cluster centroids stop moving significantly

Sum of squared errors (SSE) increases

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

There are 2 main types of HIERARCHICAL CLUSTERING​, which is agglomerative​ and division.
the key difference between Agglomerative and Division Hierarchical Clustering is,

Agglomerative starts with individual points, while Division starts with one cluster.

TRUE

FALSE

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which type of clustering is often used in natural language processing?

Euclidean Distance

Manhattan Distance

Cosine Similarity

Tension-Vector Similarity

5.

MULTIPLE SELECT QUESTION

30 sec • 1 pt

Which of the following is a common linkage criterion used in Hierarchical Clustering? (Select all that apply)

Single Linkage

Complete Linkage

Mean Squared Linkage

Integration Linkage

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

In Spectral Clustering, what is the purpose of computing the Graph Laplacian Matrix?

To randomly assign data points to clusters

To transform the data into a lower-dimensional space for better clustering

To determine the number of clusters directly

To eliminate noise from the dataset before clustering

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following is valid in steps of K-means algorithms?

Initialization, assignment, update, and convergence.
Initialization, evaluation, classification, and finalization.
Selection, classification, evaluation, and termination.
Clustering, sorting, filtering, and completion.

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