【Python】scikit-learn机器学习（八）——K-means聚类

数据描述

KMeans函数介绍

代码实现

import numpy as np
from sklearn.cluster import KMeans

def loadData(filePath):
fr = open(filePath,'r+')
lines = fr.readlines()
retData = []
retCityName = []
for line in lines:
items = line.strip().split(",")
retCityName.append(items[0])
retData.append([float(items[i]) for i in range(1,len(items))])
return retData,retCityName

if __name__ == '__main__':
data,cityName = loadData('city.txt')
km = KMeans(n_clusters=4)
label = km.fit_predict(data)
expenses = np.sum(km.cluster_centers_,axis=1)
#print(expenses)
CityCluster = [[],[],[],[]]
for i in range(len(cityName)):
CityCluster[label[i]].append(cityName[i])
for i in range(len(CityCluster)):
print("Expenses:%.2f" % expenses[i])
print(CityCluster[i])

结果输出