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%)




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