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