Which clustering algorithm is particularly useful for identifying outliers in the data?Agglomerative clusteringDBSCANHierarchical clusteringK-Means
Question
Which clustering algorithm is particularly useful for identifying outliers in the data?Agglomerative clusteringDBSCANHierarchical clusteringK-Means
Solution
The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is particularly useful for identifying outliers in the data. This is because, unlike other clustering algorithms such as K-Means, Hierarchical, and Agglomerative clustering, DBSCAN has the ability to find arbitrary shaped clusters and it's good at separating high density clusters from low density clusters. It can identify points that are not part of any cluster very well, which are typically considered as outliers or noise.
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