こんにちは。
DBSCANアルゴリズムとは¹、データクラスタリングの一種で、近傍探索（例）と素集合データ構造法との組み合わせに基づいています。

自分でも、scikit-learnのソース²を読み、ほぼ引き写しで DBSCANアルゴリズム（depth-first型とbreadth-first型の二種）を書いてみました。

念のためscikit-learnによる結果と一致するかを検算しました。

$ ./dbscan.py
True : depth-first
True : breadth-first

dbscan.py

# !/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import print_function
import numpy as np
import sklearn.cluster
from scipy import spatial

def dbscandepth(neigh, minp):
    ilabel = 0
    labels = [-1 for _ in neigh]  # initialized as noise
    for i in range(len(neigh)):
        if labels[i] >= 0 or len(neigh[i]) < minp:  # visited or noise
            continue
        candid = [i]
        while len(candid) > 0:
            i = candid.pop()
            if len(neigh[i]) >= minp:  # core only
                for j in neigh[i]:
                    if labels[j] < 0:
                        candid.append(j)
                        labels[j] = ilabel
        ilabel += 1
    return labels

def dbscanbreadth(neigh, minp):
    def expandcandid(candid):
        candid = filter(lambda j: len(neigh[j]) >= minp, candid)  # core only
        if candid != []:
            candid = [neigh[j] for j in candid]
            candid = list(set.union(*map(set, candid)))
            candid = filter(lambda j: labels[j] < 0, candid)
        return candid
    ilabel = 0
    labels = [-1 for _ in neigh]  # initialized as noise
    for i, candid in enumerate(neigh):
        if labels[i] >= 0 or len(candid) < minp:  # visited or noise
            continue
        while len(candid) > 0:
            candid = expandcandid(candid)
            for j in candid:
                labels[j] = ilabel
        ilabel += 1
    return labels

def main():
    minp = 4
    radius = 0.1
    points = np.random.uniform(size=(100,2))
    labels_sklearn = list(sklearn.cluster.DBSCAN(radius, minp).fit(points).labels_  # scikit-learn

    tree = spatial.cKDTree(points)
    neigh = [tree.query_ball_point(p, radius) for p in points]  # 近傍探索

    labels = dbscandepth(neigh, minp)
    print(labels == labels_sklearn, ": depth first")
    labels = dbscanbreadth(neigh, minp)
    print(labels == labels_sklearn, ": breadth first")
    return 0

if __name__ == '__main__':
    main()

ここにも記事があります："DBSCAN Blues" ↩
ソースは _dbscan_inner.pyx と dbscan_.py ↩