Which clustering algorithm is particularly useful for identifying outliers in the data?DBSCANHierarchical clusteringK-MeansAgglomerative clusteringClear selection
Question
Which clustering algorithm is particularly useful for identifying outliers in the data?DBSCANHierarchical clusteringK-MeansAgglomerative clusteringClear selection
Solution
The DBSCAN clustering algorithm is particularly useful for identifying outliers in the data. This is because DBSCAN, which stands for Density-Based Spatial Clustering of Applications with Noise, is designed to discover clusters of different shapes and sizes, and it can identify noise or outliers in the dataset. Unlike other clustering methods such as K-Means or Hierarchical clustering, DBSCAN does not require the user to specify the number of clusters in advance, and it can capture clusters of complex shapes, making it more effective at identifying outliers.
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