DBSCAN, and sklearn realization

基本概念:(Density-Based Spatial Clustering of Application with Noiso)

1. Core Objects:

If a point density reaches a threshold algorithm set it as the core point. (I.e., the number of points in the field of not less than MinPts r)

FIELD 2.ε- distance threshold:

Set radius r

3. Direct density of up to:

If a point p in the field of point q r, and q is the core points density-reachable directly pq

4. density of up to:

If a point sequence q0, Ql, ... qk, for any qi-qi-q are density-reachable directly, called up from q0 to qk density, the density of which is in fact directly reachable "communication . "

5. Density is connected:

If starting from a core point p, q point and k are density-reachable point, called the point q and k is the density of points connected

6. boundary points:

Non-core point belong to a class, we can not develop off the assembly line

7. Direct density of up to:

If a point p in the field of point q r, and q is the core points density-reachable directly pq

8. Noise Point:

Does not belong to any class cluster of points, starting from any point is a core density is not reachable

9. The visual display:

A: Core Objects

B, C: boundary point

N: outlier

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Origin www.cnblogs.com/liuwenhua/p/11618927.html