In hierarchical clustering, which linkage method considers the maximum distance between points in the clusters when merging two clusters?Average linkageComplete linkageWard linkage Single linkage

The ______________ linkage criterion is a popular choice for hierarchical clustering, which merges the two clusters based on the mean distance between their points.Select one:a.Centroidb.Singlec.Averaged.Complete

37.What is true about complete linkage in hierarchical clustering?  A. We merge in each step the 2 clusters whose closest members have member have smallest distance  B. the inter cluster distance is defined as the shortest distance between two points (one point in each cluster)  C. distance between 2 clusters is defined as average distance between each points in one cluster to every point in other cluster  D. Clusters are separated by sparse regions based on density

In hierarchical clustering, what does the 'ward' linkage criterion aim to minimise?Number of clustersTotal within-cluster varianceMaximum distance between clustersSum of squared distances from each point to the nearest cluster centroid

Which of the following is NOT a common method used in hierarchical clustering? Single linkage Complete linkage Average linkage K-means linkage1 pointIn K-means clustering, how is the number of clusters (K) typically determined? It is always set to 5 by default By minimizing the within-cluster sum of squares. Using methods like the elbow method or silhouette analysis. None of these1 pointWhat is the primary purpose of Discriminant Analysis (DA)? To find the mean of multiple groups of observations. To separate two or more groups of observations based on selected variables. To find the correlation between two variables. To determine the standard deviation of a single group of observations.1 pointWhich statement about Discriminant Analysis (DA) is TRUE? DA is used when the groups are defined after the study. The end result of DA is a model for predicting the mean of the selected variables. DA works by finding one or more linear combinations of the selected variables. DA is primarily used to find the correlation between two groups.1 pointWhich of the following best describes the function of Discriminant Analysis (DA) when predicting or allocating new observations? It uses a nonlinear function to assign each individual to a predefined group. It relies solely on the mean of the selected variables for group assignment. It uses either a linear or quadratic function to assign each individual to one of the predefined groups. It randomly assigns each individual to a predefined group based on probability.1 pointWhich of the following best describes the primary function of Discriminant Analysis (DA)? To predict the mean of multiple groups of observations. To divide two or more groups of observations based on measured variables. To correlate multiple groups of observations. To determine the standard deviation of a single group of observations.1 pointWhich type of Discriminant Analysis does NOT assume that the classes have equal covariance? Linear Discriminant Analysis (LDA) Quadratic Discriminant Analysis (QDA) Both LDA and QDA Neither LDA nor QDA1 pointWhich of the following is a challenge faced by researchers when collecting primary data in rural areas? Villagers always prefer online surveys. Rural people are often suspicious of urban folk with questionnaires. Villagers always prefer one-to-one interviews. Rural areas always have a large number of respondents.1 pointIn rural market research, which of the following is NOT a reason for conducting consumer research? Identifying potential customers and segments. Finding motivation to use products. Predicting global stock market trends. Developing marketing strategy.1 pointWhat is a crucial first step before starting the data collection process in international market research? Analyzing the data Reporting the findings. Defining your objectives. Choosing your methods.

In agglomerative hierarchical clustering, what happens when two clusters are merged?The original clusters remain unchangedThe number of clusters increases by oneThe distance matrix is updated to reflect the new clusterThe algorithm terminates