In hierarchical clustering, approaches such as ________ are most often used when numerical variables are analyzed.
A) Matching coefficient or the Manhattan distance
B) Jaccard's coefficient or the Euclidean distance
C) the Euclidean distance or the Manhattan distance
D) Matching coefficient or Jaccard's coefficient

Question

Quizplus · Accepted Answer

Hierarchical clustering often utilizes distance measures to quantify the similarity or dissimilarity between data points. The Euclidean distance and the Manhattan distance are two common metrics used for numerical variables, as they effectively capture the geometric or absolute differences between points in a numerical space.

In Hierarchical Clustering, Approaches Such as ________ Are Most Often