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K-Means Quiz

Authored by Dr. 1229

Mathematics

University

Used 8+ times

K-Means Quiz
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10 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is k-means clustering?

K-means clustering is a regression algorithm.

K-means clustering is a machine learning algorithm used to partition a dataset into groups or clusters based on their similarity.

K-means clustering is used to classify images.

K-means clustering is a supervised learning algorithm.

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What are the algorithm steps of k-means clustering?

Assign centroids, initialize data points, recalculate centroids, repeat until convergence

Assign data points, initialize centroids, recalculate centroids, repeat until convergence

Recalculate centroids, assign data points, initialize centroids, repeat until convergence

Initialize centroids, assign data points, recalculate centroids, repeat until convergence

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How is the number of clusters chosen in k-means clustering?

By randomly selecting a number of clusters

By using techniques such as the elbow method or silhouette analysis.

By using the average silhouette width

By choosing the number of clusters based on the maximum within-cluster sum of squares

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What are some common methods for evaluating k-means clustering?

random assignment

hierarchical clustering

elbow method, silhouette coefficient, and gap statistic

dendrogram analysis

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What are some applications of k-means clustering?

Customer segmentation, image compression, document clustering, anomaly detection, and recommendation systems

Speech recognition, network traffic analysis, and social media analysis

Text classification, fraud detection, and market segmentation

Image recognition, sentiment analysis, and time series forecasting

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

In k-means clustering, what is the role of the centroid?

The centroid is a point that represents the average of all data points in the cluster.

The centroid is a random point chosen as the center of the cluster.

The centroid represents the center of a cluster.

The centroid is a data point that is closest to all other points in the cluster.

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the objective function used in k-means clustering?

Sum of mean distances

Sum of squared distances

Sum of squared errors

Sum of absolute distances

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