KMeans is a centroid-based clustering algorithm that partitions data into a predefined number of clusters by minimizing the sum of squared distances between data points and their corresponding cluster centroids. DBSCAN, on the other hand, is a density-based clustering algorithm that identifies clusters based on the density of data points in a region. The main differences between KMeans and DBSCAN are: 1. KMeans requires the number of clusters to be specified in advance, while DBSCAN automatically determines the number of clusters based on the data"s density. 2. KMeans is sensitive to the initial placement of centroids and may converge to a local minimum, while DBSCAN does not have this issue. 3. KMeans assumes that clusters are spherical and have similar sizes, while DBSCAN can identify clusters of arbitrary shapes and sizes. 4. DBSCAN is more robust to outliers compared to KMeans.