dbscan

Density-based spatial clustering of applications with noise (DBSCAN), 基于密度的聚类方法。
算法的阈值只有两个，距离阈值eps， 最小点个数minPts。如果一个点在eps为半径的园内，包含点的个数>=minPts,那么这个点以及这个圆内的点可以作为一个cluster。
由于需要获取某个点附近k个点，所以会用kdtree做查询优化。

DBSCAN(DB, distFunc, eps, minPts) {
    C := 0                                                  /* Cluster counter */
    for each point P in database DB {
        if label(P) ≠ undefined then continue               /* Previously processed in inner loop */
        Neighbors N := RangeQuery(DB, distFunc, P, eps)     /* Find neighbors */
        if |N| < minPts then {                              /* Density check */
            label(P) := Noise                               /* Label as Noise */
            continue
        }
        C := C + 1                                          /* next cluster label */
        label(P) := C                                       /* Label initial point */
        SeedSet S := N \ {P}                                /* Neighbors to expand */
        for each point Q in S {                             /* Process every seed point Q */
            if label(Q) = Noise then label(Q) := C          /* Change Noise to border point */
            if label(Q) ≠ undefined then continue           /* Previously processed (e.g., border point) */
            label(Q) := C                                   /* Label neighbor */
            Neighbors N := RangeQuery(DB, distFunc, Q, eps) /* Find neighbors */
            if |N| ≥ minPts then {                          /* Density check (if Q is a core point) */
                S := S ∪ N                                  /* Add new neighbors to seed set */
            }
        }
    }
}

RangeQuery(DB, distFunc, Q, eps) {
    Neighbors N := empty list
    for each point P in database DB {                      /* Scan all points in the database */
        if distFunc(Q, P) ≤ eps then {                     /* Compute distance and check epsilon */
            N := N ∪ {P}                                   /* Add to result */
        }
    }
    return N
}

- dbscan python实现

https://github.com/lyyiangang/python_test/blob/master/dbscan/dbscan.py

• kdtree python 实现
  https://github.com/lyyiangang/python_test/blob/master/dbscan/kdtree.py

• cpp 实现
  https://github.com/Eleobert/dbscan
  https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html
  https://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py