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  • weka聚类预测

    > java weka.clusterers.SimpleKMeans -p 1 -l G:Programdata_Factoryexample.model -T G:Programdata_Factorysave_file_ID2Class.arff
    0 1 (0)
    1 2 (0)
    2 1 (0)
    3 3 (57)
    4 1 (0)
    > java weka.clusterers.SimpleKMeans -l G:Programdata_Factoryexample.model -T G:Programdata_Factorysave_file_ID2Class.arff
    kMeans
    ======
    Number of iterations: 8
    Within cluster sum of squared errors: 252.54315798169944
    Missing values globally replaced with mean/mode
    Cluster centroids:
                             Cluster#
    Attribute    Full Data          0          1          2          3
                     (200)        (9)      (139)       (29)       (23)
    ==================================================================
    H00               7.66    57.5556     2.9281     3.3793    22.1304
    H01              3.265    45.5556     0.1799          0     9.4783
    H02              2.015    28.4444     0.1007          0     5.7826
    H03               1.96    19.6667     0.2734          0     7.6957
    H04              1.505    17.6667     0.3957     0.4828     3.1739
    H05               1.13    13.1111          0     0.8621     3.6087
    H06              1.855          8     1.1583     2.0345     3.4348
    H07               2.49     6.5556     1.0719     5.1724      6.087
    H08               3.51     7.5556     0.5899    14.1724     6.1304
    H09              5.295    18.5556      0.223    21.8966     9.8261
    H10               7.12    23.6667     0.8921         26    14.4783
    H11              8.195    25.2222     0.7194    24.7931    25.7826
    H12             10.505    20.7778      1.554    29.7241    36.3478
    H13             11.245     7.2222     2.3381    30.7241     42.087
    H14              10.32     0.3333     4.5396    11.1724     48.087
    H15              10.55          0     4.8993     7.2069    53.0435
    H16               9.71          0     4.8921     4.5517    49.1304
    H17              10.72     5.6667     5.7914     8.0345    45.8696
    H18             12.315          0     7.2518    15.6552    43.5217
    H19             14.185          0    10.0647    16.2759         42
    H20              16.68          0    12.8417    25.2414    35.6087
    H21              18.07     4.3333    15.4748    22.7241    33.2609
    H22             16.875    15.6667    13.1511    19.4483    36.6087
    H23              7.375    14.6667     4.4173     7.5172    22.2174
    === Clustering stats for training data ===
    === Clustering stats for testing data ===
    Clustered Instances
    1      3 ( 60%)
    2      1 ( 20%)
    3      1 ( 20%)




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  • 原文地址:https://www.cnblogs.com/iathena/p/15b76e87dd67292eadc00cdb2dd0ffdc.html
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