DBSCAN vs. K-means

What's the Difference?

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and K-means are both popular clustering algorithms used in data mining and machine learning. However, they differ in their approach and characteristics. K-means is a centroid-based algorithm that aims to partition the data into a predetermined number of clusters by minimizing the sum of squared distances between data points and their assigned cluster centroids. On the other hand, DBSCAN is a density-based algorithm that groups together data points based on their density and connectivity. DBSCAN does not require the number of clusters to be specified in advance and can discover clusters of arbitrary shapes. While K-means is sensitive to initial centroid placement and can be influenced by outliers, DBSCAN is more robust to noise and can handle clusters of varying densities.

Comparison

Further Detail

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