DBSCAN is an acronym for a clustering algorithm that arranges data points based on their proximity and density, which differs from approaches such as k-means, which require the user to predetermine the cluster count. DBSCAN, on the other hand, examines the data to identify areas of high density and separates them from sparser regions. This is accomplished by tracing a neighborhood around each data point and grouping them into the same cluster when a significant number of points cluster tightly together, indicating high density. Data points in low density locations that do not fit into any cluster are considered noise. This property makes DBSCAN particularly useful for finding clusters of various forms and sizes, as well as managing datasets with inherent noise.
K-means is a clustering technique that divides a dataset into a set number of clusters or groups. The technique starts by randomly selecting ‘k’ beginning points known as ‘centroids.’ Each data point is then assigned to the nearest centroid, and new centroids are generated as the average of all points inside the cluster using these assignments. This process of allocating data points to the nearest centroid and updating centroids is repeated until the centroids vary just slightly. The result is ‘k’ clusters in which data points within the same cluster are closer to one another than points in other clusters. It is essential for the user to specify the ‘k’ value in advance, representing the desired number of clusters.