Which of the following statements regarding clustering are true?<br /> A. 1. DBSCAN can identify noise points in addition to clusters. <br />B. 2. DBSCAN requires the user to specify the number of clusters beforehand. <br />C. 3. Hierarchical clustering results depend on distance and linkage criteria. <br />D. 4. K-means clustering guarantees convergence to the global optimum.
Question
Which of the following statements regarding clustering are true?<br /> A. 1. DBSCAN can identify noise points in addition to clusters. <br />B. 2. DBSCAN requires the user to specify the number of clusters beforehand. <br />C. 3. Hierarchical clustering results depend on distance and linkage criteria. <br />D. 4. K-means clustering guarantees convergence to the global optimum.
Solution 1
A. 1. True - DBSCAN (Density-Based Spatial Clustering of Applications with Noise) can indeed identify noise points in addition to clusters. It does this by classifying data points as core points, reachable points, or noise points.
B. 2. False - Unlike K-means clustering, DBSCAN does not require the user to specify the number of clusters beforehand. Instead, it uses a density-based approach to form clusters and identify outliers.
C. 3. True - The results of Hierarchical clustering do depend on the distance (or similarity) and linkage criteria used. Different choices of distance measures (e.g., Euclidean, Manhattan, etc.) and linkage criteria (e.g., single, complete, average, etc.) can lead to different clustering results.
D. 4. False - K-means clustering does not guarantee convergence to the global optimum. It is a heuristic algorithm that finds a local optimum, not necessarily the global one. The final result can depend on the initial choice of centroids.
Solution 2
A. 1. True - DBSCAN (Density-Based Spatial Clustering of Applications with Noise) can indeed identify noise points in addition to clusters. It does this by classifying data points as core points, reachable points, or noise points.
B. 2. False - Unlike K-means clustering, DBSCAN does not require the user to specify the number of clusters beforehand. Instead, it uses a density-based approach to form clusters and identify outliers.
C. 3. True - The results of Hierarchical clustering do depend on the distance (how the distance between clusters is measured) and linkage criteria (how the proximity of clusters is calculated). Different choices of distance and linkage can lead to different clustering results.
D. 4. False - K-means clustering does not guarantee convergence to the global optimum. It is a heuristic algorithm that finds a local optimum, not necessarily the global one. The final result can depend on the initial placement of centroids.
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