Basé sur la densité Regroupement is a popular clustering technique in analyse de données and apprentissage automatique that identifies groups of similar data points based on their density in a espace de caractéristiques. Unlike traditional clustering methods like K-moyennes, which assume spherical cluster shapes and require the number of clusters to be specified in advance, Density-Based Clustering can discover clusters of arbitrary shapes and sizes.
L'idée centrale de cette approche est de regrouper ensemble les points de données qui sont étroitement regroupés tout en marquant comme valeurs aberrantes les points qui se trouvent seuls dans des régions de faible densité. Cela est particulièrement utile dans les scénarios où les clusters peuvent avoir des formes irrégulières ou lorsqu'il faut gérer du bruit dans les données.
L'une des méthodes les plus courantes algorithms used for Density-Based Clustering is DBSCAN (Density-Based Spatial Clustering of Applications with Noise). DBSCAN operates by defining a neighborhood around each data point within a specified radius (epsilon) and counting the number of points in that neighborhood. If this count exceeds a predefined threshold (minPts), the point is considered a core point and a cluster is formed. Neighboring points that are also within the radius of core points are subsequently added to the cluster. Points that do not belong to any clusters are classified as noise.
Density-Based Clustering is particularly effective in applications such as geographical data analysis, anomaly detection, and segmentation d'image, where the distribution of data is complex and not easily separable with linear boundaries.