密度に基づく クラスタリング is a popular clustering technique in データ分析 and 機械学習 that identifies groups of similar data points based on their density in a 特徴空間. Unlike traditional clustering methods like K-means, 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.
このアプローチの核心的なアイデアは、密集しているデータポイントをまとめ、低密度の領域に孤立しているポイントを外れ値としてマークすることです。これは、クラスタが不規則な形状をしている場合や、データのノイズを扱う場合に特に有用です。
最も一般的なものの一つ 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 画像セグメンテーション, where the distribution of data is complex and not easily separable with linear boundaries.