Which clustering algorithm identifies overlapping clusters?
In clustering analysis, certain algorithms are better suited for identifying overlapping clusters in data. Traditional clustering methods like K-Means (a) and Agglomerative Clustering (c) typically assign each data point to a single cluster, making them unsuitable for scenarios where clusters overlap. DBSCAN (b) is a density-based algorithm but is not specifically designed to handle overlapping clusters. For identifying overlapping clusters, Density-Based Overlapping Clustering (d) is the appropriate choice, as it is specifically designed to recognize and manage clusters that share data points 📊🔄.
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