Unsupervised Learning Flashcards
7 cards from real Machine Learning practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Unsupervised Learning flashcards as text
Which problem can arise when using K-Means with very high-dimensional data?
Answer: The curse of dimensionality makes Euclidean distances less meaningful
In high dimensions, distances between points become increasingly similar (concentration of measure), making it hard for K-Means to distinguish meaningful clusters.
What is 'spectral clustering' and what advantage does it have?
Answer: It uses eigenvectors of a similarity graph Laplacian to cluster data of non-convex shapes
Spectral clustering constructs a similarity graph, computes its Laplacian eigenvectors, then clusters the low-dimensional embedding, allowing non-convex cluster detection.
In Self-Organizing Maps (SOMs), what is the 'neighborhood function' responsible for?
Answer: Updating weights of neurons near the winning neuron to preserve topological structure
The neighborhood function ensures that neurons near the winning (best-matching) unit are also updated, causing the map to preserve topological relationships of the input data.
What is 'co-clustering' (biclustering) in unsupervised learning?
Answer: Simultaneously clustering both rows and columns of a data matrix
Biclustering finds subsets of rows and columns in a matrix that exhibit coherent patterns, commonly used in gene expression analysis.
Why is random initialization in K-Means a concern, and how does K-Means++ address it?
Answer: Random init can lead to poor local minima; K-Means++ spreads initial centroids to improve convergence
K-Means++ selects initial centroids with probability proportional to their distance from already-chosen centroids, reducing the chance of converging to a bad local minimum.
What does the Davies-Bouldin Index measure in cluster evaluation?
Answer: The ratio of within-cluster scatter to between-cluster separation; lower is better
The Davies-Bouldin Index computes the average similarity ratio of each cluster with its most similar cluster; a lower score indicates better-defined, well-separated clusters.
Which unsupervised technique is most suitable for finding association rules like 'customers who buy X also buy Y'?
Answer: Apriori algorithm (frequent itemset mining)
The Apriori algorithm mines frequent itemsets from transaction data and derives association rules based on support, confidence, and lift metrics.
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