Basado en Densidad Agrupamiento is a popular clustering technique in análisis de datos and aprendizaje automático that identifies groups of similar data points based on their density in a espacio de características. Unlike traditional clustering methods like K-medias, 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.
La idea central de este enfoque es agrupar puntos de datos que están muy juntos, marcando como valores atípicos aquellos puntos que se encuentran solos en regiones de baja densidad. Esto es particularmente útil en escenarios donde los clústeres pueden tener formas irregulares o cuando se trata de ruido en los datos.
Uno de los métodos más comunes 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 segmentación de imágenes, where the distribution of data is complex and not easily separable with linear boundaries.