PAML_session5_Unsupervised_Learning

What is the primary characteristic of unsupervised learning?

• It uses labeled data for training

• It predicts future values based on past data

• It finds patterns and relationships in unlabeled data

• It requires explicit output values

Which of the following is NOT a clustering algorithm?

• K-Means

• Hierarchical Clustering

• Apriori

• DBSCAN

What is the primary goal of clustering in unsupervised learning?

• To classify data into predefined categories

• To find relationships between variables

• To group similar data points together

• To train a predictive model

Which of the following clustering algorithms does NOT require specifying the number of clusters in advance?

• K-Means

• DBSCAN

• K - Medoids

• Gaussian Mixture Model (GMM)

In K-Means clustering, what determines the formation of clusters?

• 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

Which metric is NOT used in association rule learning?

• Support

• Confidence

• Lift

• Variance

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

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

• DBSCAN

• PCA

• FP-Growth

• K-Means

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

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

• Group similar items into clusters

• Find relationships between frequent itemsets

• Reduce the dimensionality of data while preserving variance

• Detect anomalies in datasets