Data Science Unsupervised Learning Techniques Questions and Answers Flashcards

What problem does the 'elbow method' help solve when using K-Means clustering?

Answer: Determining the optimal number of clusters

The elbow method plots within-cluster sum of squares against the number of clusters and identifies the point where adding more clusters yields diminishing returns.

Which unsupervised learning algorithm groups data points by modeling them as generated from a mixture of probability distributions?

Answer: Gaussian Mixture Models (GMM)

GMM assumes data is generated from a mixture of several Gaussian distributions with unknown parameters, using expectation-maximization to estimate them.

What is a key advantage of DBSCAN over K-Means clustering?

Answer: It can discover clusters of arbitrary shape without specifying the number of clusters

DBSCAN identifies clusters based on density, allowing it to find arbitrarily shaped clusters and automatically determine the number of clusters.

In association rule mining, what does the 'lift' metric indicate when its value is greater than 1?

Answer: The items appear together more often than expected by chance

A lift value greater than 1 indicates a positive association, meaning the items co-occur more frequently than would be expected if they were independent.

Which technique uses an autoencoder neural network for unsupervised feature learning?

Answer: Training a network to reconstruct its input through a compressed hidden layer

Autoencoders learn compressed representations by training a neural network to reconstruct its own input through a bottleneck hidden layer.

What does the term 'inertia' refer to in K-Means clustering?

Answer: The sum of squared distances between each point and its assigned cluster centroid

Inertia measures the total within-cluster sum of squares, quantifying how internally coherent the clusters are.