Baseado em Densidade Agrupamento is a popular clustering technique in dados útil and aprendizado de máquina that identifies groups of similar data points based on their density in a espaço de características. Unlike traditional clustering methods like K-médias, 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.
A ideia central por trás dessa abordagem é agrupar pontos de dados que estão próximos uns dos outros, marcando como outliers pontos que estão isolados em regiões de baixa densidade. Isso é particularmente útil em cenários onde os clusters podem ter formas irregulares ou ao lidar com ruído nos dados.
Uma das mais comuns 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 segmentação de imagem, where the distribution of data is complex and not easily separable with linear boundaries.