PAML_session5_Unsupervised_Learning

Front

What is the primary characteristic of unsupervised learning?

Back

It finds patterns and relationships in unlabeled data

Front

Which of the following is NOT a clustering algorithm? K-Means, Hierarchical Clustering, Apriori, DBSCAN

Back

Apriori

Front

What is the primary goal of clustering in unsupervised learning?

Back

To group similar data points together

Front

Which of the following clustering algorithms does NOT require specifying the number of clusters in advance? K-Means, K - Medoids, Gaussian Mixture Model (GMM)

Back

DBSCAN

Front

In K-Means clustering, what determines the formation of clusters? Options: The distance between points and cluster centroids, The density of points in an area, The number of data dimensions, The presence of labels in data

Back

The distance between points and cluster centroids

Front

Which metric is NOT used in association rule learning? Support, Confidence, Lift, Variance

Back

Variance

Front

Which of the following statements is true about DBSCAN? It requires specifying the number of clusters beforehand, It handles noise better than K-Means, It only works well for spherical clusters, It does not use density-based clustering

Back

It handles noise better than K-Means

Front

Which algorithm is an optimized version of the Apriori algorithm for association rule learning?

Back

FP-Growth

Front

Which of the following is a primary challenge in unsupervised learning? Having too much labeled data, Lack of interpretability of discovered patterns, The need for explicit outputs, High dependency on human supervision

Back

Lack of interpretability of discovered patterns

Front

What does Principal Component Analysis (PCA) primarily aim to do?

Back

Reduce the dimensionality of data while preserving variance