Test your knowledge.Receive immediate feedback.You find all answers in the book. Quiz | Cluster Analysis /27 50 Quiz | Cluster Analysis 1 / 27 Why is cluster analysis considered related to exploratory data analysis procedures? It is used for predictive modeling. It leads to suggestions for grouping objects and discovering structures in datasets. It helps identify the standard deviation of data. It calculates the mean values of variables. 2 / 27 How can you determine the number of clusters? K-Means Dendrogramm Agglomeration schedule 3 / 27 What are the two types of hierarchical clustering? Partioning clustering Divisive clustering Agglomerative clustering 4 / 27 What does the Euclidean distance in a cluster analysis primarily consider? Similarity between objects Correlation between objects Dissimilarity between objects Absolute differences between objects 5 / 27 Why is the single-linkage method considered suitable for identifying outliers? It tends to form chains, making it effective at detecting outliers. It forms a few large groups with many small ones "left over". It is not suitable for detecting outliers. It uses the largest value of individual distances. 6 / 27 How do agglomerative and divisive hierarchical clustering methods differ? Agglomerative methods are based on a broad partition, while divisive methods form different groups from a granular partition. Agglomerative methods are faster than divisive methods. Agglomerative methods form different groups from a broad partition, while divisive methods divide a full sample into different groups. Divisive methods are more commonly used in practice than agglomerative methods. 7 / 27 What factors should be considered when selecting cluster variables for analysis? The selection of a clustering method The relevance, independence, and measurability of variables, among others The number of clusters to be formed The variables with the highest correlations 8 / 27 What is the primary purpose of cluster analysis? To calculate the mean value of a dataset To identify the standard deviation of a dataset To increase data heterogeneity To merge objects into comparable groups based on similarities 9 / 27 What is one of the ways to process nominally scaled variables in cluster analysis? Convert them into ordinal variables Use them as-is without any modification Transform them into binary variables Calculate their means and variances 10 / 27 What is the purpose of selecting an appropriate proximity measure in cluster analysis? To determine the number of clusters To quantify the similarity or dissimilarity between objects To decide which variables are relevant for clustering To calculate the mean values of variables 11 / 27 In cluster analysis, what is "intragroup homogeneity"? The number of groups formed The degree of dissimilarity within groups The degree of dissimilarity between groups The degree of similarity within groups 12 / 27 How are proximity measures typically categorized in cluster analysis? As dissimilarity measures only As similarity measures only As either similarity or distance measures As measures of variable correlations 13 / 27 What is the main characteristic of dilating clustering procedures? They tend to form chains by merging individual objects. They group objects into individual groups of approximately equal size. They show no tendency to dilate or contract. They form a few large groups with many small ones "left over". 14 / 27 When should cluster analysis be used instead of factor analysis? Check 15 / 27 How is the similarity or dissimilarity between objects determined in cluster analysis? By performing discriminant analysis By conducting a factor analysis By calculating the mean values of variables By using proximity measures 16 / 27 What is the first step in performing a cluster analysis? Determination of the number of clusters Selection of cluster variables Interpretation of a cluster solution Selection of the clustering method 17 / 27 In single-linkage clustering (nearest neighbor), how is the distance between a newly formed cluster and an object calculated? By taking the mimimum of the distances between the objects in the cluster and the object By taking the sum of the distances between the objects in the cluster and the object By taking the average of the distances between the objects in the cluster and the object By taking the maximum of the distances between the objects in the cluster and the object 18 / 27 In cluster analysis, what does the Minkowski metric generalize? The determination of the number of clusters The Euclidean distance and city block metric The selection of cluster variables The Pearson correlation coefficient 19 / 27 How can the number of clusters in hierarchical cluster analysis be determined using the elbow criterion? By applying k-means cluster analysis By comparing the results of different clustering methods By calculating t-values for each variable By identifying a "leap" in the values of the heterogeneity measure 20 / 27 What is the aim of cluster analysis? Check 21 / 27 What is the main objective of cluster analysis? To group similar objects into clusters based on similarities To analyze causal relationships between variables To study correlations between metric and nominal variables To reduce the number of variables in a data set 22 / 27 When transforming a nominal variable into binary variables, what does the value '1' typically represent? "Attribute value exists" "Attribute value does not exist" "Attribute value is uncertain" "Attribute value is missing" 23 / 27 What algorithm can be used to detect outliers? Single-linkage algorithm Complete-linkage algorithm Ward algorithm 24 / 27 What is the key criterion for clustering objects in Ward's method? Maximizing the variance between clusters Minimizing the sum of squared distances within each cluster Maximizing the number of clusters Minimizing the total number of objects in each cluster 25 / 27 What is the primary advantage of Ward's method in cluster analysis? It often finds good partitionings and correctly assigns elements to groups. It works best when variables are correlated. It forms chains of objects. It is suitable for identifying outliers. 26 / 27 What is the purpose of calculating t-values and F-values in cluster analysis? To identify outliers To assess the quality of a clustering solution and characterize the clusters To determine the number of clusters To apply the elbow criterion 27 / 27 Complete-linkage clustering (furthest neighbor) calculates distances between clusters by: Taking the sum of the distances between objects in the clusters. Taking the maximum of the distances between objects in the clusters. Taking the average of the distances between objects in the clusters. Taking the minimum of the distances between objects in the clusters. Your score is 0% Restart quiz Learn more…MethodsServiceAbout us ContactFeedbackOrder data etc. GeneralImprintPrivacy notice