What distinguishes hierarchical agglomerative clustering from k-means clustering?

Question

BlackTom AI · Accepted Answer

When evaluating clustering methods, a variety of characteristics come into play across different algorithms.
Option 1: 'It requires specifying the number of clusters beforehand' is a trait commonly associated with k-means, not hierarchical agglomerative clustering. Hierarchical methods do not require a predefined number of clusters; they produce a dendrogram that can be cut at any level to obtain a chosen number of clusters.
Option 2: 'It builds clusters from the bottom up, starting with individual data points' accurately describes hierarchical agglomerative clustering (HAC). HAC starts with each data point as its own singleton cluster and iteratively merges the closest pairs until a stopping criterion is met, forming a hierarchy.
Option 3: 'It uses random initialization of cluster centers' is characteristic of k-means, where random initial centroids can influence the final clustering result. HAC does not rely on centroid initialization at all since it merges clusters based on proximity measures.
Option 4: 'It always produces spherical clusters' is a property often associated with certain implementations or assumptions of k-means in Euclidean space, which tends to favor spherical cluster shapes. HAC does not assume spherical clusters; the shapes depend on the linkage method and data structure, and can be quite flexible.
Thus, the distinguishing feature of HAC among the options is its bottom-up, agglomerative process, starting with all points as separate clusters and merging upward, rather than requiring a predefined number of clusters or relying on random centroid initialization or a spherical-cluster bias.

What distinguishes hierarchical agglomerative clustering from k-means clustering?单项选择题

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