H13-311_V3.5 · Question #219
What are the common clustering algorithms?
The correct answer is A. Density clustering B. Hierarchical clustering C. Spectral clustering D. Kmeans. All four options represent well-established families of clustering algorithms, making this an "all of the above" question. K-means (D) is the most foundational: it partitions data into K groups by minimizing within-cluster variance, iteratively assigning points to the nearest…
Question
What are the common clustering algorithms?
Options
- ADensity clustering
- BHierarchical clustering
- CSpectral clustering
- DKmeans
How the community answered
(69 responses)- A100% (69)
Explanation
All four options represent well-established families of clustering algorithms, making this an "all of the above" question.
- K-means (D) is the most foundational: it partitions data into K groups by minimizing within-cluster variance, iteratively assigning points to the nearest centroid.
- Hierarchical clustering (B) builds a tree (dendrogram) of nested clusters either bottom-up (agglomerative) or top-down (divisive), useful when the number of clusters is unknown.
- Density clustering (A) - most commonly DBSCAN - groups points based on density regions, naturally handling irregular shapes and identifying outliers as noise.
- Spectral clustering (C) uses graph theory and eigenvalues of a similarity matrix to cluster data that isn't linearly separable in its original space.
There are no distractors here - all four are legitimate, widely-taught algorithms covered in any standard ML curriculum.
Memory tip: Use the acronym KHDS - K-means, Hierarchical, Density, Spectral - and remember they represent four different "strategies": distance-to-centroid, tree structure, region density, and graph connectivity.
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