Data Mining Quiz 2

Which of the following is not typically an unsupervised learning technique?

• K-Means

• PCA

• DBSCAN

• Linear Discriminant Analysis (LDA)

PCA transforms the data to:

• A lower-dimensional space with dependent features

• A higher-dimensional space

• A lower-dimensional space with uncorrelated features

• The original feature space with normalized values

A key disadvantage of K-Means is:

• It can find non-linear boundaries

• It is deterministic

• It requires labeled data

• It assumes clusters are spherical and equal in size

Which evaluation metric is used for clustering when labels are unavailable?

• Accuracy

• Adjusted Rand Index

• Silhouette Score

• ROC-AUC

Which algorithm is best suited for discovering clusters with varying density?

• K-Means

• PCA

• DBSCAN

• Hierarchical clustering

Which of the following is true for hierarchical clustering?

• It cannot be visualized

• It doesn’t require a distance metric

• It always produces the same number of clusters

• It can be agglomerative or divisive

The curse of dimensionality affects:

• Linear regression performance

• K-Means efficiency and distance accuracy

• Label encoding in supervised models

• Ensemble model performance

What does t-SNE do?

• Clusters data points

• Classifies data

• Projects high-dimensional data into 2 or 3 dimensions for visualization

• Removes noise from data

In PCA, what determines how many components should be selected?

• Number of classes

• Explained variance ratio

• Number of original features

• Training error

Which technique can detect anomalies in data using unsupervised learning?

• K-Nearest Neighbors

• DBSCAN

• Logistic Regression

• Random Forest