Which clustering algorithm can handle datasets with noise and outliers?Review LaterK-MeansAgglomerative Hierarchical ClusteringDBSCANDivisive Clustering
Question
Which clustering algorithm can handle datasets with noise and outliers?Review LaterK-MeansAgglomerative Hierarchical ClusteringDBSCANDivisive Clustering
Solution
The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is well-suited for datasets with noise and outliers. This is because, unlike K-Means and Hierarchical Clustering, DBSCAN does not require the user to specify the number of clusters in advance, and it can discover clusters of arbitrary shape, not just spherical ones like K-Means. Furthermore, DBSCAN identifies outliers as a separate cluster, thus handling noise in the dataset.
Similar Questions
Which clustering algorithm is particularly useful for identifying outliers in the data?Agglomerative clusteringDBSCANHierarchical clusteringK-Means
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.
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