Chapter 4.1 - Descriptive Data Mining
Complete linkage defines the similarity between two clusters as the similarity of the pair of observations (one from each cluster) that
are the most different
The goal of _____________ is to segment observations into similar groups based on the observed variables.
clustering
Complete linkage is a measure of calculating dissimilarity between clusters by considering only the two closest observations in the two clusters.
false
__________________ assigns each observation to one of k clusters in a manner such that the observations assigned to the same cluster are as similar as possible.
k-means clustering
________________________ analyzes items frequently co-occuring in transactions (such as purchases).
market basket analysis
Single linkage is a measure of calculating the distance between two clusters by considering only the two _______________ observations between the two clusters.
most similar
Euclidean distance can be used to measure the distance between ________________ in cluster analysis.
observations
If the Euclidean distance were to be represented in a right triangle, which of the following would be considered the distance between two objects of a cluster?
the hypotenuse
Centroid linkage uses the averaging concept of cluster centroids to define between-cluster similarity.
true
Euclidean distance is the most common method to measure dissimilarity between observations.
true
Ward's method merges two clusters such that the dissimilarity of the observations within the resulting single cluster increases _______________.
as little as possible
The ____________________ clustering method defines the similarity between two clusters as the similarity of the pair of observations (one from each cluster) that are the most different.
complete linkage
__________________ is bottom-up clustering that starts with each observation belonging to its own cluster and then sequentially merges the most similar clusters to create a series of nested clusters.
hierarchical clustering
Complete linkage is a measure of calculating dissimilarity between clusters by considering only the two most dissimilar observations in the two clusters.
true
Euclidean distance can be used to measure the distance between two observations each consisting of two variable measurements.
true