Question 2According to the DBSCAN required inputs, which statement describes the n_clu input?1 pointIt's the function to calculate distance.It's the radius of local neighborhood.It determines density threshold (for fixed Ɛ) (The minimum amount of points for a particular point to be consider a core point of a cluster).It's the maximum amount of observations for a particular point to be consider a core point of a cluster.

Question 3How is a core point defined in the DBSCAN algorithm?1 pointAn Ɛ-neighbor point than has fewer than n_clu neighbors itself.A point that has the same amount of n_clu neighbors within and outside the Ɛ-neighborhood.A point that has more than n_clu neighbors in their Ɛ-neighborhood.A point that has no points in its Ɛ-neighborhood.

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

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.

Clustering result obtained from DBSCAN is _____ .

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.