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 as Euclidean distance. A point is considered a core point if it has at least a minimum number of other points (minPts) within its neighborhood. Points that are within the neighborhood of a core point but do not have enough neighbors to be core points themselves are classified as border points. Points that do not meet the criteria for core or border points are considered noise.

DBSCAN forms clusters by starting with an arbitrary point and retrieving all points density-reachable from it. If a point is a core point, its neighborhood is expanded, and the process continues recursively. This process is repeated for all points in the dataset, resulting in the formation of clusters. Points that are not density-reachable from any core point are classified as noise.

One of the key advantages of DBSCAN is its ability to find arbitrarily shaped clusters and handle noise effectively. However, it can struggle with varying densities within the same dataset and requires careful selection of the distance metric and parameters (epsilon and minPts) to perform optimally. Despite these challenges, DBSCAN remains a valuable tool for clustering tasks in various domains, including spatial data analysis, image processing, and anomaly detection.