K-Means and DBSCAN are two distinct clustering algorithms.
K-Means is a partition-based clustering method where you need to specify the number of clusters (K) beforehand, and it assigns data points to the nearest cluster centroid.
However, it can be sensitive to the initial placement of centroids and primarily performs hard clustering, meaning each point belongs to one cluster. K-Means also assumes that clusters are spherical in shape.
On the other hand, DBSCAN is a density-based clustering algorithm that automatically identifies clusters based on data density without requiring you to specify the number of clusters in advance.
It excels at identifying clusters of arbitrary shapes, handles noise or outliers, and can accommodate clusters of varying sizes.
While DBSCAN primarily performs hard clustering, there are extensions available to achieve soft clustering when necessary.
In summary, the choice between K-Means and DBSCAN depends on factors like the knowledge of the number of clusters and the data’s characteristics, as they have different strengths and use cases in the realm of data clustering.
