Cuestionario vinculado a una guía · Medio Nivel

Prueba de agrupación jerárquica

Lea las alturas de los dendrogramas, distinga las reglas de vinculación y comprenda cómo las opciones de escala y corte afectan los resultados de agrupación jerárquica.

Rutas de guía relacionadasAgrupación jerárquica
Pregunta 1 de 8

What sequence does agglomerative hierarchical clustering build?

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  1. What sequence does agglomerative hierarchical clustering build?
    • Repeated cluster merges from individual observations toward larger groups
    • Repeatedly split one cluster into smaller groups from the top down
    • Assign each point to a fixed centroid and update those centroids
    • Join each point to its nearest neighbor once without building nested groups
  2. Which rule defines single linkage between two clusters?
    • The maximum pairwise distance across clusters
    • The minimum pairwise distance across clusters
    • The average of all pairwise distances
    • The increase in within-cluster sum of squares
  3. Which linkage uses the farthest cross-cluster pair?
    • Average linkage
    • Single linkage
    • Complete linkage
    • Ward linkage
  4. What does a dendrogram merge height encode?
    • The p-value for a test that the two groups differ
    • The number of observations contained in the merged branch
    • The proportion of observations assigned to the left child
    • The linkage distance or criterion value at that merge
  5. Why can a large jump in dendrogram height be useful?
    • It proves the exact number of natural populations
    • It may suggest candidate cuts that keep separated branches apart
    • It means every leaf is an outlier
    • It identifies the optimal feature scaling
  6. Why may single linkage create a chaining pattern?
    • It requires all points to be equally distant
    • It uses the average distance between every pair
    • A sequence of near neighbors can connect otherwise elongated groups
    • It minimizes centroid variance
  7. What does Ward linkage seek to minimize when merging?
    • The entropy of labels
    • The largest single pairwise distance only
    • The number of leaves in the tree
    • The increase in within-cluster sum of squares
  8. Why standardize features before Euclidean hierarchical clustering when units vary greatly?
    • Otherwise a large-scale feature can dominate distances
    • Scaling changes the dendrogram into a supervised model
    • It forces average linkage to equal Ward linkage
    • It guarantees that the chosen cut is stable