【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 npfrom 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