True or false: The primary advantage of using DBSCAN for clustering in geospatial analysis is its ability to find clusters of varying shapes and sizes without specifying the number of clusters beforehand.TrueFalse

Question 1Which of the following statements is a characteristic of the DBSCAN algorithm?1 pointCan handle tons of data and weird shapes.Finds uneven cluster sizes (one is big, some are tiny).It will do a great performance finding many clusters. It will do a great performance finding few clusters

What is a disadvantage of density-based clustering methods like DBSCAN?Answer areaIt is sensitive to the number of clustersIt cannot handle noise in the dataIt requires specifying density parameters like epsilon and minimum pointsIt assumes clusters are convex

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.

Consider the following code snippet for clustering geospatial data using DBSCAN: from sklearn.cluster import DBSCANimport numpy as np coordinates = np.array([    [37.77, -122.42],    [37.78, -122.41],    [37.76, -122.43],    [37.74, -122.44],    [37.73, -122.45]]) db = DBSCAN(eps=0.01, min_samples=2).fit(coordinates)labels = db.labels_print(labels) What do the resulting labels indicate about the clustering of the coordinates?All points belong to the same cluster.Each point is assigned to a unique cluster.Points are divided into clusters with at least one noise point.All points are classified as noise.

Define the DBSCAN algorithm and its key parameters. Explore the notionof density-based clustering and how DBSCAN handles noise. Illustratesituations where DBSCAN outperforms other clustering methods.