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A data scientist is clustering a data set but does not want to specify the number of clusters present. Which of the following algorithms should the data scientist use?
Correct Answer: A
# DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that does not require specifying the number of clusters in advance. It identifies clusters of arbitrary shape and separates noise/outliers based on density thresholds. Why other options are incorrect: * B: k-NN is a supervised classification algorithm, not used for clustering. * C: k-means requires predefining the number of clusters (k). * D: Logistic regression is a classification model, not for clustering. Official References: * CompTIA DataX (DY0-001) Study Guide - Section 4.2:"DBSCAN detects clusters based on data density without the need for a predefined k value and handles outliers effectively." -
