DBSCANmenetelmä
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular clustering algorithm in data mining and machine learning. It was introduced by Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu in 1996. Unlike many other clustering algorithms, DBSCAN does not require the number of clusters to be specified in advance. Instead, it identifies clusters based on the density of data points in the feature space.
The algorithm works by defining a neighborhood around each data point, typically using a distance metric such
DBSCAN forms clusters by starting with an arbitrary point and retrieving all points density-reachable from it.
One of the key advantages of DBSCAN is its ability to find arbitrarily shaped clusters and handle