测试数据展示：
#coding:utf-8 __author__ = 'similarface' ''' 实现K均值算法 算法摘要： －－－－－－－－－－－－－－－－－－－－－－－－－－－－－ 输入：所有数据点A，聚类个数k 输出：k个聚类的中心点 随机选取k个初始的中心点 repeat: 计算每个点和中心点的距离，将点分配给最近的中心簇中 计算Ck，更新簇的中心点 until 中心点稳定 －－－－－－－－－－－－－－－－－－－－－－－－－－－－－ ''' import sys import random import math from collections import defaultdict data=[[ -1.97869191 ,-10.41036729], [ -5.98733291 , 1.09319552], [ -3.32042501 , -9.43521984], [ -6.95990143 , 1.48643384], [ -0.51581431 , -2.95589335], [ -6.98576681 , 2.3764377 ], [ -5.80752805 , -0.01880673], [ -2.3875164 , -3.79858985], [ -1.50834952 ,-11.55108763], [ -0.31932001 , -8.72310502], [ -0.10604775 , -1.97508279], [ -6.3698932 , 2.17096658], [ 0.98564842 , -2.22738281], [ -0.9889725 , -3.47291703], [ -1.03011438 , -1.02557957], [ -0.45064353 , -8.8508534 ], [ 0.72986148 , -1.97091264], [ -0.24707289 , -9.8104778 ], [ -3.30332765 , 3.32133548], [ 1.02581503 , -2.93411237], [ -0.77207963 , -3.88246058], [ 1.11435514 , -2.1403809 ], [ -3.01806146 , -8.44065141], [ 2.07905596 , -0.39137275], [ -1.3132102 , -5.21553485], [ 2.37703059 , -1.1452029 ], [ -3.71486953 , -9.36874886], [ -0.44554402 , -3.2977466 ], [ -1.07589398 , -8.07477912], [ -7.2774513 , 3.49894912], [ -1.29923245 , -8.30647414], [ -1.39638106 , -4.26919995], [ -2.23638886 , -5.40208811], [ -2.50153112 , -7.67699872], [ -0.72081785 , -1.37019171], [ -5.89699295 , 2.86422394], [ -0.66995787 , -9.05797846], [ -6.2936531 , 1.50084162], [ -3.14819261 , -7.7502907 ], [ 0.77192861 , -2.46670777], [ -1.38115313 , -1.23503221], [ -5.21920316 , 2.77710219], [ -2.18234803 ,-10.07244764], [ -1.80391665 , -5.05103832], [ 0.67755635 , -2.64355425], [ -0.52813711 ,-10.47523635], [ -4.22948326 , 1.0542576 ], [ 1.7408485 , -1.52644915], [ -0.6172497 , -0.79361238], [ -6.17304838 , 1.40648868], [ -2.4369522 , -3.95312369], [ -2.33805418 ,-10.39048298], [ -0.89823572 , -8.91519992], [ -5.49292357 , 1.36243111], [ -0.4075959 , -0.98602662], [ -1.46335853 , -8.46162063], [ 1.0505005 , -1.13446366], [ -6.54744623 , 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1.43966762], [ -2.53164296 , -2.76452777], [ -2.82677657 ,-11.5641273 ], [ -2.60710702 , -4.50856754], [ 0.05546421 , -9.17884603], [ -2.3788409 , -7.89698831], [ -5.90466798 , 3.09117187], [ -6.37783409 , 2.28944986], [ -6.3933459 , 2.05685086], [ 0.68989568 , -3.88908243], [ 0.51326445 , -2.31320125], [ -1.44144678 , -2.6282341 ], [ -2.69891251 , -9.73324948], [ -0.28764562 , -0.98370587], [ 0.25165836 ,-11.46732114], [ -4.95384992 , 2.04017736], [ -0.85715442 , -0.96306408], [ 2.10389484 , -1.66689096], [ -6.81038823 , 2.37777702], [ 0.78454593 , -1.15593416], [ -1.45368824 , -3.27385342], [ -0.37638912 , 1.3767851 ], [ -5.06831433 , 2.53524728], [ -2.70131918 , -9.63497056], [ -1.23856256 ,-10.59940081], [ -1.93958449 , -2.98186006], [ -0.30387455 , -3.25837812], [ -4.98980684 , 3.66124623], [ -2.84011639 , -3.47084983], [ -1.68584182 , -3.25767216], [ -0.58390398 , -8.78405909], [ -0.78216181 , -9.35497119], [ -5.58569152 , 1.43897246], [ -1.65427904 , -4.34620073], [ -1.38595406 , -3.46417994], [ 0.57884096 , -1.22623874], [ 2.03872755 , 0.07546388]] def calc_geometric_distance(pointA,pointB): ''' 计算两个点的几何距离 兼容多纬度 :param pointA: :param pointB: :return: ''' if len(pointA)==len(pointB): sumct = 0 for idx in range(len(pointA)): sumct += math.pow(pointA[idx]-pointB[idx],2) return math.sqrt(sumct) return None def getInitCenter(data,k): ''' 随机选择k个点做中心点 :param data: :param k: :return: ''' initcenter=[] for i in range(k): idx=random.randint(0,len(data)) initcenter.append(data[idx]) return initcenter def cluser(dataset): ''' 将单个点 向 所有中心点距离最近的归属 :param dataset: :return: ''' k_dataset=defaultdict(list) for k,v in dataset.items(): k_dataset[v.index(min(v))+1].append(k) return k_dataset def cluser_data(data,data_idx): ''' 还原数据 开始存放的是数据的索引 :param data: 数据 :param data_idx: 数据索引集合 :return: ''' data_cluster={} for k,v in data_idx.items(): data_cluster[k]=[data[item] for item in v] return data_cluster def calc_center(clusterdataset): ''' 计算中心点 :param clusterdataset: :return: ''' centers=[] #遍历每一个簇的数据 for k,v in clusterdataset.items(): #单个簇的数据个数 numct=float(len(v)) #将单个簇的数据对应特征求和 sum_vecter=[0]*len(v[0]) for item in v: for ix in range(len(sum_vecter)): sum_vecter[ix]=sum_vecter[ix]+item[ix] #单个簇的均值 avg_vector=[item/numct for item in sum_vecter] #为中心点 centers.append(avg_vector) return centers def k_means(data,initcenter,i): distances=defaultdict(list) for idx_data in range(len(data)): item=data[idx_data] #点到所有中心点几何 for center in initcenter: #点到中心点距离 point_center_d=calc_geometric_distance(item,center) distances[idx_data].append(point_center_d) initcenter=calc_center(cluser_data(data,cluser(distances))) print initcenter,i i=i+1 if i>100: sys.exit(-1) k_means(data,initcenter,i) # print distances return None if __name__=='__main__': k=4 i=0 initcenter=getInitCenter(data,k) k_means(data,initcenter,i) ''' 数据在第次收敛： [[-1.090354490561798, -9.544300641123598], [-3.4009604193589746, -3.448081201923078], [-0.19541685830769231, -3.3116750070769227], [-2.446104015535713, 0.7224692077976191]] 0 [[-1.2749446563000004, -9.409093471199999], [-2.470575770652174, -3.807719888695652], [0.35515101615942024, -2.3586021581159424], [-5.119257947586205, 1.9765036246551722]] 1 [[-1.2749446563000004, -9.409093471199999], [-1.967171268292682, -3.7557153614634147], [0.8185451994915249, -1.6050642896610166], [-5.937498566799999, 2.2599845357999997]] 2 [[-1.2749446563000004, -9.409093471199999], [-1.7570419110638291, -3.732020352340426], [0.9475681998113205, -1.3826069122641511], [-5.937498566799999, 2.2599845357999997]] 3 [[-1.2749446563000004, -9.409093471199999], [-1.6700011342424235, -3.7007944505050507], [0.9961425923762373, -1.2969068833663366], [-5.937498566799999, 2.2599845357999997]] 4 [[-1.2749446563000004, -9.409093471199999], [-1.6660748016999996, -3.6790574597], [1.0188776970999995, -1.2946049985], [-5.937498566799999, 2.2599845357999997]] 5 [[-1.2749446563000004, -9.409093471199999], [-1.6660748016999996, -3.6790574597], [1.0188776970999995, -1.2946049985], [-5.937498566799999, 2.2599845357999997]] 6 ''