instance：
我们先对此数据集进行轮廓系数的计算
from sklearn import metrics
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn import preprocessing
import pandas as pd def import_data_format_iris(file):
"""
file这里是输入文件的路径，如iris.txt.
格式化数据，前四列为data，最后一列为类标号（有0，1，2三类）
如果是你自己的data，就不需要执行此段函数了。
"""
data = []
cluster_location = []
with open(str(file), 'r') as f:
for line in f:
current = line.strip().split(",") # 对每一行以逗号为分割，返回一个list
current_dummy = []
for j in range(0, len(current) - 1):
current_dummy.append(float(current[j])) # current_dummy存放data
j += 1
# 下面注这段话提供了一个范例：若类标号不是0，1，2之类数字时该怎么给数据集
# 归类
if current[j] == "Iris-setosa\n":
cluster_location.append(0)
elif current[j] == "Iris-versicolor\n":
cluster_location.append(1)
else:
cluster_location.append(2)
data.append(current_dummy)
print("加载数据完毕")
return data # data = pd.read_csv('C:\\Users\\Style\\Desktop\\Iris.csv')
data = import_data_format_iris('C:\\Users\\Style\\Desktop\\Iris.csv')
info_scaled = preprocessing.scale(data)
X = info_scaled
score = []
for i in range(2, 18):
km = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)
km.fit(X)
score.append(metrics.silhouette_score(X, km.labels_, metric='euclidean'))
plt.figure(dpi=150)
plt.plot(range(2, 18), score, marker='o')
plt.xlabel('Number of clusters')
plt.ylabel('silhouette_score')
plt.show()
得到图像
看得出来 当簇数为2的时候点最高
接下来 用模糊C均值聚类
import copy
import math
import random
import time global MAX # 用于初始化隶属度矩阵U
MAX = 10000.0 global Epsilon # 结束条件
Epsilon = 0.0000001 def import_data_format_iris(file):
"""
file这里是输入文件的路径，如iris.txt.
格式化数据，前四列为data，最后一列为类标号（有0，1，2三类）
如果是你自己的data，就不需要执行此段函数了。
"""
data = []
cluster_location = []
with open(str(file), 'r') as f:
for line in f:
current = line.strip().split(",") # 对每一行以逗号为分割，返回一个list
current_dummy = []
for j in range(0, len(current) - 1):
current_dummy.append(float(current[j])) # current_dummy存放data
j += 1
# 下面注这段话提供了一个范例：若类标号不是0，1，2之类数字时该怎么给数据集
# 归类
if current[j] == "Iris-setosa\n":
cluster_location.append(0)
elif current[j] == "Iris-versicolor\n":
cluster_location.append(1)
else:
cluster_location.append(2)
data.append(current_dummy)
print("加载数据完毕")
return data # return data , cluster_location def randomize_data(data):
"""
该功能将数据随机化，并保持随机化顺序的记录
"""
order = list(range(0, len(data)))
random.shuffle(order)
new_data = [[] for i in range(0, len(data))]
for index in range(0, len(order)):
new_data[index] = data[order[index]]
return new_data, order def de_randomise_data(data, order):
"""
此函数将返回数据的原始顺序，将randomise_data()返回的order列表作为参数
"""
new_data = [[] for i in range(0, len(data))]
for index in range(len(order)):
new_data[order[index]] = data[index]
return new_data def print_matrix(list):
"""
以可重复的方式打印矩阵
"""
for i in range(0, len(list)):
print(list[i]) def initialize_U(data, cluster_number):
"""
这个函数是隶属度矩阵U的每行加起来都为1. 此处需要一个全局变量MAX.
"""
global MAX
U = []
for i in range(0, len(data)):
current = []
rand_sum = 0.0
for j in range(0, cluster_number):
dummy = random.randint(1, int(MAX))
current.append(dummy)
rand_sum += dummy
for j in range(0, cluster_number):
current[j] = current[j] / rand_sum
U.append(current)
return U def distance(point, center):
"""
该函数计算2点之间的距离（作为列表）。我们指欧几里德距离。闵可夫斯基距离
"""
if len(point) != len(center):
return -1
dummy = 0.0
for i in range(0, len(point)):
dummy += abs(point[i] - center[i]) ** 2
return math.sqrt(dummy) def end_conditon(U, U_old):
"""
结束条件。当U矩阵随着连续迭代停止变化时，触发结束
"""
global Epsilon
for i in range(0, len(U)):
for j in range(0, len(U[0])):
if abs(U[i][j] - U_old[i][j]) < Epsilon:
return False
return True def normalise_U(U):
"""
在聚类结束时使U模糊化。每个样本的隶属度最大的为1，其余为0
"""
for i in range(0, len(U)):
maximum = max(U[i])
for j in range(0, len(U[0])):
if U[i][j] != maximum:
U[i][j] = 0
else:
U[i][j] = 1
return U # m的最佳取值范围为[1.5，2.5]
def fuzzy(data, cluster_number, m):
"""
这是主函数，它将计算所需的聚类中心，并返回最终的归一化隶属矩阵U.
参数是：簇数(cluster_number)和隶属度的因子(m)
"""
# 初始化隶属度矩阵U
U = initialize_U(data, cluster_number)
# print_matrix(U)
# 循环更新U
while (True):
# 创建它的副本，以检查结束条件
U_old = copy.deepcopy(U)
# 计算聚类中心
C = []
for j in range(0, cluster_number):
current_cluster_center = []
for i in range(0, len(data[0])):
dummy_sum_num = 0.0
dummy_sum_dum = 0.0
for k in range(0, len(data)):
# 分子
dummy_sum_num += (U[k][j] ** m) * data[k][i]
# 分母
dummy_sum_dum += (U[k][j] ** m)
# 第i列的聚类中心
current_cluster_center.append(dummy_sum_num / dummy_sum_dum)
# 第j簇的所有聚类中心
C.append(current_cluster_center) # 创建一个距离向量, 用于计算U矩阵。
distance_matrix = []
for i in range(0, len(data)):
current = []
for j in range(0, cluster_number):
current.append(distance(data[i], C[j]))
distance_matrix.append(current) # 更新U
for j in range(0, cluster_number):
for i in range(0, len(data)):
dummy = 0.0
for k in range(0, cluster_number):
# 分母
dummy += (distance_matrix[i][j] / distance_matrix[i][k]) ** (2 / (m - 1))
U[i][j] = 1 / dummy if end_conditon(U, U_old):
print("结束聚类")
break
print("标准化 U")
U = normalise_U(U)
return U def checker_iris(final_location):
"""
和真实的聚类结果进行校验比对
"""
right = 0.0
for k in range(0, 3):
checker = [0, 0, 0]
for i in range(0, 50):
for j in range(0, len(final_location[0])):
if final_location[i + (50 * k)][j] == 1: # i+(50*k)表示 j表示第j类
checker[j] += 1 # checker分别统计每一类分类正确的个数
right += max(checker) # 累加分类正确的个数
print('分类正确的个数是:', right)
answer = right / 150 * 100
return "准确率：" + str(answer) + "%" if __name__ == '__main__':
# 加载数据
data = import_data_format_iris("C:\\Users\\Style\\Desktop\\Iris.csv")
# print_matrix(data) # 随机化数据
data, order = randomize_data(data)
# print_matrix(data) start = time.time()
# 现在我们有一个名为data的列表，它只是数字
# 我们还有另一个名为cluster_location的列表，它给出了正确的聚类结果位置
# 调用模糊C均值函数
final_location = fuzzy(data, 2, 2) # 还原数据
final_location = de_randomise_data(final_location, order)
# print_matrix(final_location) # 准确度分析
print(checker_iris(final_location))
print("用时：{0}".format(time.time() - start))
得到
加载数据完毕
结束聚类
标准化 U
分类正确的个数是: 126.0
准确率：84.0%
用时：0.0029931068420410156
