Test your knowledge.Receive immediate feedback.You find all answers in the book. Quiz | Cluster Analysis /37 57 Quiz | Cluster Analysis 1 / 37 What is the main characteristic of dilating clustering procedures? They tend to form chains by merging individual objects. They show no tendency to dilate or contract. They group objects into individual groups of approximately equal size. They form a few large groups with many small ones "left over". 2 / 37 What algorithm can be used to detect outliers? Complete-linkage algorithm Single-linkage algorithm Ward algorithm 3 / 37 In k-means clustering, what is the target criterion for forming clusters? Minimum variance within clusters Minimum variance between clusters Maximum variance within clusters Maximum variance between clusters 4 / 37 How do agglomerative and divisive hierarchical clustering methods differ? 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. 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. 5 / 37 What factors should be considered when selecting cluster variables for analysis? The variables with the highest correlations The selection of a clustering method The number of clusters to be formed The relevance, independence, and measurability of variables, among others 6 / 37 What is the first step in a cluster analysis once the cluster variables have been determined? Selecting the proximity measure and fusion algorithm Deciding the number of clusters Applying the Ward method Creating a distance matrix between all cases 7 / 37 What is the primary purpose of cluster analysis? To merge objects into comparable groups based on similarities To identify the standard deviation of a dataset To increase data heterogeneity To calculate the mean value of a dataset 8 / 37 Why is the single-linkage method considered suitable for identifying outliers? It uses the largest value of individual distances. It forms a few large groups with many small ones "left over". It is not suitable for detecting outliers. It tends to form chains, making it effective at detecting outliers. 9 / 37 Which cluster fusion algorithm is known to provide fairly good partitions and often indicates the correct number of clusters? Average linkage Complete linkage Single linkage Ward method 10 / 37 How can the number of clusters in hierarchical cluster analysis be determined using the elbow criterion? By identifying a "leap" in the values of the heterogeneity measure By calculating t-values for each variable By comparing the results of different clustering methods By applying k-means cluster analysis 11 / 37 What is the aim of cluster analysis? Check 12 / 37 What is one limitation of agglomerative cluster procedures, especially for large case numbers? They are computationally efficient They always yield accurate results They work well with small datasets They require calculating a distance matrix for each clustering step 13 / 37 What is the main objective of cluster analysis? To analyze causal relationships between variables To study correlations between metric and nominal variables To group similar objects into clusters based on similarities To reduce the number of variables in a data set 14 / 37 What does the Euclidean distance in a cluster analysis primarily consider? Correlation between objects Absolute differences between objects Similarity between objects Dissimilarity between objects 15 / 37 When is it recommended to use a partitioning clustering algorithm like k-means or two-step clustering? When working with a large number of cases When the Ward method is selected When there is a need for agglomerative clustering When dealing with small datasets 16 / 37 How are proximity measures typically categorized in cluster analysis? As similarity measures only As either similarity or distance measures As dissimilarity measures only As measures of variable correlations 17 / 37 How can you determine the number of clusters? Agglomeration schedule K-Means Dendrogramm 18 / 37 What is the similarity coefficient that includes cases where both considered objects do not have certain attributes? Simple Matching (SM) similarity coefficient Russel and Rao similarity coefficient Jaccard similarity coefficient Euclidean similarity coefficient 19 / 37 What are the two types of hierarchical clustering? Agglomerative clustering Partioning clustering Divisive clustering 20 / 37 In single-linkage clustering (nearest neighbor), how is the distance between a newly formed cluster and an object calculated? By taking the maximum of the distances between the objects in the cluster and the object 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 21 / 37 What is the purpose of selecting an appropriate proximity measure in cluster analysis? To decide which variables are relevant for clustering To calculate the mean values of variables To quantify the similarity or dissimilarity between objects To determine the number of clusters 22 / 37 What is one of the ways to process nominally scaled variables in cluster analysis? Calculate their means and variances Convert them into ordinal variables Transform them into binary variables Use them as-is without any modification 23 / 37 When should cluster analysis be used instead of factor analysis? Check 24 / 37 What is the primary advantage of Ward's method in cluster analysis? It forms chains of objects. It works best when variables are correlated. It often finds good partitionings and correctly assigns elements to groups. It is suitable for identifying outliers. 25 / 37 Which similarity coefficient measures the relative proportion of common properties in relation to the number of properties that apply to at least one of the objects under consideration? Pearson similarity coefficient Simple Matching (SM) similarity coefficient Euclidean similarity coefficient Jaccard similarity coefficient 26 / 37 What is the key criterion for clustering objects in Ward's method? Minimizing the total number of objects in each cluster Maximizing the number of clusters Minimizing the sum of squared distances within each cluster Maximizing the variance between clusters 27 / 37 In the case of binary variables, what does the Simple Matching (SM) similarity coefficient count in the numerator? The number of properties common to both objects The number of properties that only one object has The total number of properties The difference between the binary values 28 / 37 What is the purpose of calculating t-values and F-values in cluster analysis? To determine the number of clusters To assess the quality of a clustering solution and characterize the clusters To apply the elbow criterion To identify outliers 29 / 37 How is the similarity or dissimilarity between objects determined in cluster analysis? By conducting a factor analysis By using proximity measures By performing discriminant analysis By calculating the mean values of variables 30 / 37 Complete-linkage clustering (furthest neighbor) calculates distances between clusters by: Taking the maximum of the distances between objects in the clusters. Taking the sum of the distances between objects in the clusters. Taking the minimum of the distances between objects in the clusters. Taking the average of the distances between objects in the clusters. 31 / 37 When transforming a nominal variable into binary variables, what does the value '1' typically represent? "Attribute value is uncertain" "Attribute value is missing" "Attribute value exists" "Attribute value does not exist" 32 / 37 Why is cluster analysis considered related to exploratory data analysis procedures? It is used for predictive modeling. It helps identify the standard deviation of data. It leads to suggestions for grouping objects and discovering structures in datasets. It calculates the mean values of variables. 33 / 37 What should researchers always consider when presenting the results of a cluster analysis? The number of clusters used should always be greater than 5. The stability of the results under different conditions. There should be several clustering variables with similar meaning. The date size should have at least 5,000 observations. 34 / 37 Why might several iterations be required in a cluster analysis? To avoid using proximity measures To confuse the results To save computational time To achieve a meaningful interpretation of the results 35 / 37 What is the first step in performing a cluster analysis? Selection of the clustering method Selection of cluster variables Determination of the number of clusters Interpretation of a cluster solution 36 / 37 In cluster analysis, what does the Minkowski metric generalize? The Pearson correlation coefficient The determination of the number of clusters The Euclidean distance and city block metric The selection of cluster variables 37 / 37 In cluster analysis, what is "intragroup homogeneity"? The degree of dissimilarity within groups The number of groups formed The degree of similarity within groups The degree of dissimilarity between groups Your score is 0% Restart quiz Learn more…MethodsServiceAbout us ContactFeedbackOrder data etc. GeneralImprintPrivacy notice
