机器学习：【学习笔记】K-Means算法

实现过程

1 建立工程，导入sklearn相关包

import numpy as np
from sklearn.cluster import KMeans

2 加载数据，创建算法实例

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=3)
    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])

调用K-Means方法所需要的参数：

• n_clusters:用于指定聚类中心的个数
• init:初始聚类中心的初始化方法
• max_iter:最大的迭代次数
• data:加载的数据
• label:聚类后各数据所属的标签
• fit_predic():计算簇中心以及为簇分配序号

【注意】：默认使用的是欧式距离