初识聚类算法: 基本K均值，初识聚类k均值,基本K均值 ：选择 K

初识聚类算法: 基本K均值，初识聚类k均值,基本K均值 ：选择 K

基本K均值 ：选择 K 个初始质心，其中 K 是用户指定的参数，即所期望的簇的个数。每次循环中，每个点被指派到最近的质心，指派到同一个质心的点集构成一个。然后，根据指派到簇的点，更新每个簇的质心。重复指派和更新操作，直到质心不发生明显的变化。

# scoding=utf-8import pylab as plpoints = [[int(eachpoint.split("#")[0]), int(eachpoint.split("#")[1])] for eachpoint in open("points","r")]# 指定三个初始质心currentCenter1 = [20,190]; currentCenter2 = [120,90]; currentCenter3 = [170,140]pl.plot([currentCenter1[0]], [currentCenter1[1]],'ok')pl.plot([currentCenter2[0]], [currentCenter2[1]],'ok')pl.plot([currentCenter3[0]], [currentCenter3[1]],'ok')# 记录每次迭代后每个簇的质心的更新轨迹center1 = [currentCenter1]; center2 = [currentCenter2]; center3 = [currentCenter3]# 三个簇group1 = []; group2 = []; group3 = []for runtime in range(50):    group1 = []; group2 = []; group3 = []    for eachpoint in points:        # 计算每个点到三个质心的距离        distance1 = pow(abs(eachpoint[0]-currentCenter1[0]),2) + pow(abs(eachpoint[1]-currentCenter1[1]),2)        distance2 = pow(abs(eachpoint[0]-currentCenter2[0]),2) + pow(abs(eachpoint[1]-currentCenter2[1]),2)        distance3 = pow(abs(eachpoint[0]-currentCenter3[0]),2) + pow(abs(eachpoint[1]-currentCenter3[1]),2)        # 将该点指派到离它最近的质心所在的簇        mindis = min(distance1,distance2,distance3)        if(mindis == distance1):            group1.append(eachpoint)        elif(mindis == distance2):            group2.append(eachpoint)        else:            group3.append(eachpoint)    # 指派完所有的点后，更新每个簇的质心    currentCenter1 = [sum([eachpoint[0] for eachpoint in group1])/len(group1),sum([eachpoint[1] for eachpoint in group1])/len(group1)]    currentCenter2 = [sum([eachpoint[0] for eachpoint in group2])/len(group2),sum([eachpoint[1] for eachpoint in group2])/len(group2)]    currentCenter3 = [sum([eachpoint[0] for eachpoint in group3])/len(group3),sum([eachpoint[1] for eachpoint in group3])/len(group3)]    # 记录该次对质心的更新    center1.append(currentCenter1)    center2.append(currentCenter2)    center3.append(currentCenter3)# 打印所有的点，用颜色标识该点所属的簇pl.plot([eachpoint[0] for eachpoint in group1], [eachpoint[1] for eachpoint in group1], 'or')pl.plot([eachpoint[0] for eachpoint in group2], [eachpoint[1] for eachpoint in group2], 'oy')pl.plot([eachpoint[0] for eachpoint in group3], [eachpoint[1] for eachpoint in group3], 'og')# 打印每个簇的质心的更新轨迹for center in [center1,center2,center3]:    pl.plot([eachcenter[0] for eachcenter in center], [eachcenter[1] for eachcenter in center],'k')pl.show()#该片段来自于http://byrx.net