基于密度的聚类算法是一种非常重要的聚类算法，也是本文后面章节将着重介绍

The density-based clustering algorithm is a very important clustering algorithm, and it is also an algorithm that will be introduced and expanded in later chapters of this article. The key idea of ​​density-based clustering algorithm is to extract high-density sets isolated by low-density sets. The difference from the distance-based clustering algorithm is that the clustering result of the distance-based clustering algorithm is spherical clusters, but the density-based clustering algorithm can get all possible shapes of clusters, which is mixed with noise It has a good effect when you click the object.

Density-based clustering algorithm is a very important clustering algorithm, which will be introduced and expanded in the later sections of this paper. The key idea of density-based clustering algorithms is to extract high-density collections isolated by low-density collections. The difference between distance-based clustering algorithms is that the clustering results in spherical classes, but density-based clustering algorithms can get classes of all possible shapes, which work well when they are used on objects doped with noise points.

Density based clustering algorithm is a very important clustering algorithm, and it will be introduced and expanded in the later chapter of this paper. The key idea of density based clustering algorithm is to extract high density sets isolated by low density sets. The difference between distance based clustering algorithm and distance based clustering algorithm is that the clustering result of distance based clustering algorithm is spherical class, but density based clustering algorithm can get all possible shape classes, which has good effect when it acts on the objects with noise points.<br>