Quiz on Agglomerative Methods in Machine Learning

Introduction to Agglomerative Methods Answers 1. What does the height of the fusion points in a dendrogram indicate? A. The number of clusters formed B. The number of data points in each cluster C. The similarity of the merged clusters D. The distance between clusters Answer: The similarity of the merged clusters (C) The height of the fusion points represents the similarity between the merged clusters. 2. Which method is used to evaluate the quality of clustering by calculating silhouette coefficients? A. Gap statistic B. Silhouette analysis C. Elbow method D. Variance method Answer: Silhouette analysis (B) Silhouette analysis assesses the quality of clustering by calculating 'silhouette coefficients' for each data point. 3. Why is feature scaling important in agglomerative clustering? A. It helps in visualizing the dendrogram clearly B. It ensures all features contribute equally to distance calculations C. It eliminates the need for handling missing data D. It clusters the data points based solely on magnitude Answer: It ensures all features contribute equally to distance calculations (B) Feature scaling ensures that features with larger magnitudes do not dominate the distance calculations. 4. What does the elbow method help identify in clustering? A. The specific linkage criteria to use B. The total number of data points required for clustering C. The number of clusters where the rate of decrease plateaus D. The optimal distance measure for clusters Answer: The number of clusters where the rate of decrease plateaus (C) The elbow method identifies the point where the rate of decrease in distances or linkage criteria plateaus. 5. How does the choice of linkage criterion affect clustering results? A. It influences how clusters are merged based on data characteristics B. It determines the number of clusters created C. It does not impact the clustering structure significantly D. It regulates the speed of the clustering algorithm Answer: It influences how clusters are merged based on data characteristics (A) The linkage criterion significantly affects the clustering results by influencing how clusters are merged. 6. What is the primary approach used by agglomerative methods in clustering? A. Randomized merging of clusters B. Iterative separation of clusters C. Bottom-up approach D. Top-down approach Answer: Bottom-up approach (C) Agglomerative methods utilize a bottom-up approach to build a hierarchy of clusters. 7. Which linkage criterion is most likely to create elongated or chain-like clusters? A. Centroid linkage B. Single linkage C. Average linkage D. Complete linkage Answer: Single linkage (B) Single linkage measures the shortest distance between any two points in different clusters, which can lead to elongated clusters. 8. What is one of the key advantages of using agglomerative clustering? A. Fast processing time for massive datasets B. No prior assumption about cluster shape C. Determines exact number of clusters beforehand D. Produces perfectly spherical clusters Answer: No prior assumption about cluster shape (B) Agglomerative clustering does not assume specific shapes for clusters, allowing for more flexibility. 9. What defines the termination condition in agglomerative clustering? A. When all data points are in a single cluster B. When outliers have been removed from the dataset C. When the average distance between clusters is at a minimum D. When the maximum cluster size is reached Answer: When all data points are in a single cluster (A) The agglomerative clustering process terminates when all data points are merged into one single cluster. 10. Which application is **not** commonly associated with agglomerative clustering? A. Stock price prediction B. Customer segmentation C. Image segmentation D. Document categorization Answer: Stock price prediction (A) Agglomerative clustering is typically used in areas like image segmentation, customer segmentation, and document categorization, not in stock price prediction. 11. Complete linkage in agglomerative clustering is defined by which measurement? A. The shortest distance between clusters B. The average distance of cluster points C. The distance between the centroids of clusters D. The longest distance between any two points in different clusters Answer: The longest distance between any two points in different clusters (D) Complete linkage measures the longest distance between any two data points in different clusters. 12. What is a significant disadvantage of agglomerative clustering? A. Inability to produce hierarchical structures B. Inflexibility to different cluster shapes C. Requires a large amount of labeled data D. Sensitive to outliers Answer: Sensitive to outliers (D) Agglomerative clustering is sensitive to outliers, which can affect the final merging process. 13. Average linkage is considered to be which of the following? A. A highly specialized method for outlier detection B. A method that only assesses nearest neighbors C. A compromise between single and complete linkage D. The preferred method for high-dimensional data Answer: A compromise between single and complete linkage (C) Average linkage calculates average distances and is often viewed as a good compromise between single and complete linkage.