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  • kmeans聚类算法(使用西瓜数据集4.0)

    number,density,sugercontent
    1,0.697,0.460
    2,0.774,0.376
    3, 0.634,0.264
    4,0.608,0.318
    5,0.556,0.215
    6,0.403,0.237
    7,0.481,0.149
    7,0.666,0.091
    8,0.437,0.211
    9,0.666,0.091
    10,0.243,0.267
    11,0.245,0.057
    12,0.343,0.099
    13,0.639,0.161
    14,0.657,0.198
    15,0.360,0.370
    16,0.593,0.042
    17,0.719,0.103
    18,0.359,0.188
    19,0.339,0.241
    20,0.282,0.257
    21,0.748,0.232
    22,0.714,0.346
    23,0.483,0.312
    24,0.478,0.437
    25,0.525,0.369
    26,0.751,0.489
    27,0.532,0.472
    28,0.473,0.376
    29,0.725,0.445
    30,0.446,0.459
    import numpy as np
    import matplotlib.pyplot as plt
    # Though the following import is not directly being used, it is required
    # for 3D projection to work
    from mpl_toolkits.mplot3d import Axes3D
    
    from sklearn.cluster import KMeans
    import pandas as pd
    
    
    xigua = pd.read_csv('xigua.csv')
    
    
    estimator = KMeans(n_clusters=3,max_iter=500,)
    #计算每个样本的聚类中心并预测聚类索引。
    a1=xigua.values
    print(a1[:,1:3])
    res = estimator.fit_predict(a1[:,1:3])
    #每个点的标签
    lable_pred = estimator.labels_
    #每个点的聚类中心
    centroids = estimator.cluster_centers_
    #样本距其最近的聚类中心的平方距离之和。
    inertia = estimator.inertia_
    print (lable_pred)
    print (centroids)
    print (inertia)
    
    
    for i in range(len(a1)):
        if int(lable_pred[i]) == 0:
            plt.scatter(a1[i][0], a1[i][1], color='red')
        if int(lable_pred[i]) == 1:
             plt.scatter(a1[i][0], a1[i][1], color='black')
        if int(lable_pred[i]) == 2:
            plt.scatter(a1[i][0], a1[i][1], color='yellow')
    plt.show()
    打印参数
    [2 2 1 2 1 0 0 1 0 1 0 0 0 1 1 0 1 1 0 0 0 1 2 2 2 2 2 2 2 2 2] [[0.3492 0.2076 ] [0.65311111 0.15522222] [0.6005 0.40491667]] 0.41449036111111104



    打印图片

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  • 原文地址:https://www.cnblogs.com/dudu1992/p/8954020.html
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