说明：  DBSCAN算法的Java实现 DBSCAN是一种基于密度的聚类算法，它的根基事理就是给定两个参数，ξ和minp，其中 ξ可以理解为半径，算法将在这个半径内查找样本，minp是一个以ξ为半径查找到的样本个数n的限制前提，只要n>=minp，查找到的样本点就是焦灯揭捉本点
(DBSCAN s definition of a cluster is based on the notion of density reachability. Basically, a point is directly density-reachable from a point if it is not farther away than a given distance (i.e., is part of its-neighborhood) and if is surrounded by sufficiently many points such that one may consider and be part of a cluster. is called density-reachable (note: this is different from "directly density-reachable") from if there is a sequence of points with and where each is directly density-reachable from . Note that the relation of density-reachable is not symmetric (since might lie on the edge of a cluster, having insufficiently many neighbors to count as a genuine cluster element), so the notion of density-connected is introduced: two points and are density-connected if there is a point such that both and are density reachable from . A cluster, which is a subset of the points of the database, satisfies two properties: 1.All points within the cluster are mutual)