算法的原理可参考周志华老师的《机器学习》，概念很简单，也总结了算法的流程。
下面就看一下算法的流程
密度聚类算法流程.PNG
代码比较复杂，但是完全对照算法的思路，就可看懂我的代码，一步一步按照思路理解算法。
import numpy as npfrom numpy import *import mathimport matplotlib.pyplot as pltdef load_data_set(file_name):
D = []
fr = open(file_name) for line in fr.readlines():
line_data = line.strip().split()
D.append([float(line_data[0]), float(line_data[1])]) return Ddef get_dist(x1, x2):
n = len(x1)
dist = 0
for i in range(n):
dist += (abs(float(x1[i] - x2[i]))) ** 2
return sqrt(dist)def get_partision(x1, X, sigma):
m = len(X)
N = [] for i in range(m):
dist = get_dist(x1, X[i])
print(X[i]) if(dist <= sigma):
N.append(X[i]) return Ndef list_compare(x1_list, x2_list):
n1 = len(x1_list)
n2 = len(x2_list)
equal = True
if(n1 != n2): return False
for i in range(n1): if(x1_list[i] != x2_list[i]):
equal = False
return equaldef get_intersection(x1, x2):
n = len(x2)
m = len(x1)
W = [] for i in range(m): for j in range(n): if(list_compare(x1[i], x2[j]) == True):
W.append(x1[i]) return Wdef delete_aggregate(X, x1):
n = len(x1)
m = len(X)
del_list = [] for i in range(n): for j in range(m): if(list_compare(x1[i], X[j]) == True):
del_list.append(X[j]) for j in range(len(del_list)):
print("del_list[", j, "] = ", del_list[j])
X.remove([del_list[j][0], del_list[j][1]]) return Xdef dbscan(X, sigma, min_pts):
m = len(X)
W = []
C = [] for i in range(m):
N = get_partision(X[i], X, sigma) if(len(N) >= min_pts):
W.append(X[i])
k = 0
gama = copy(X).tolist() while(len(W) != 0):
old_gama = copy(gama).tolist()
index = random.randint(0, len(W))
Q = []
Q.append(W[index]) del W[index] while(len(Q) != 0):
q = copy(Q[0]).tolist() del Q[0]
N = get_partision(q, X, sigma) if(len(N) >= min_pts):
deta = get_intersection(N, gama) for i in range(len(deta)):
Q.append(deta[i])
gama = delete_aggregate(gama, deta)
k += 1
Ck = delete_aggregate(old_gama, gama)
C.append(Ck)
W = delete_aggregate(W, Ck) return Cdef show_experiment_plot(D):
X1 = mat(array(D[0]))
X2 = mat(array(D[1]))
X3 = mat(array(D[2]))
X4 = mat(array(D[3]))
plt.plot(X1[:, 0], X1[:, 1], "or")
plt.plot(X2[:, 0], X2[:, 1], "ob")
plt.plot(X3[:, 0], X3[:, 1], "og")
plt.plot(X4[:, 0], X4[:, 1], "oy")
plt.xlabel("X1")
plt.ylabel("X2")
plt.show()if __name__ == "__main__":
D = load_data_set("data_set.txt")
C = dbscan(D, 0.11, 5)
print(D)
show_experiment_plot(C)输入数据本文的输入数据和书上的输入数据是一样的
0.697 0.4600.774 0.3760.634 0.2640.608 0.3180.556 0.2150.403 0.2370.481 0.1490.437 0.2110.666 0.0910.243 0.2670.245 0.0570.343 0.0990.639 0.1610.657 0.1980.360 0.3700.593 0.0420.719 0.1030.359 0.1880.339 0.2410.282 0.2570.748 0.2320.714 0.3460.483 0.3120.478 0.4370.525 0.3690.751 0.4890.532 0.4720.473 0.3760.725 0.4450.446 0.459
书上的输入数据
输入数据.PNG
本文的初始化参数和书上的初始化参数也是一样的
初始化参数.PNG
本文的实验结果
实验结果.PNG
书上的实验结果
书上实验结果.PNG
两者的实验结果对比，完全一模一样。本文结果中没有画出的点则是噪声，画出的则是最终的聚类结果。
作者：幸福洋溢的季节
链接：https://www.jianshu.com/p/9a99a1cd5d01
