关于weka软件EM聚类结果的分析
如题，就是怎么看做出来的结果，我看不懂结果=== Run information ===
Scheme:weka.clusterers.EM -I 100 -N -1 -M 1.0E-6 -S 100
Relation: 2
Instances: 72
Attributes: 27
a1
a2
a3
a4
a5
a6
a7
a8
a9
a10
a11
a12
a13
a14
a15
a16
a17
a18
a19
a20
a21
a22
a23
a24
a25
a26
a27
Test mode:evaluate on training data
=== Model and evaluation on training set ===
EM
==
Number of clusters selected by cross validation: 6
Cluster
Attribute 0 1 2 3 4 5
(0.18) (0.19) (0.11) (0.18) (0.14) (0.19)
============================================================
a1
mean 5.7223 5.7607 5.9588 5.5669 5.932 5.7279
std. dev. 0.0291 0.0103 0.0078 0.089 0.0125 0.0094
a2
mean 5.1738 5.3529 5.605 5.1162 5.509 5.2279
std. dev. 0.0443 0.0313 0.005 0.0334 0.0418 0.0265
a3
mean 4.3031 4.4686 4.51 4.0692 4.493 4.4079
std. dev. 0.0936 0.0091 0.0166 0.0338 0.0064 0.0157
a4
mean 4.1815 4.345 4.4813 4.1069 4.459 4.2757
std. dev. 0.0251 0.0261 0.0078 0.0406 0.0187 0.0223
a5
mean 29.2469 27.9386 26.9225 28.4462 27.231 29.0757
std. dev. 0.1186 0.1965 0.1024 0.4926 0.0503 0.2529
a6
mean 12.2731 11.2321 10.7912 12.2577 11.193 11.6507
std. dev. 0.0906 0.0291 0.0619 0.0333 0.0551 0.3252
a7
mean 13.5062 13.5629 13.8275 13.1877 14.001 13.6493
std. dev. 0.1493 0.0357 0.0386 0.1158 0.0908 0.0532
a8
mean 26.8015 26.6893 22.9325 25.1238 24.565 24.7229
std. dev. 1.4562 0.113 1.0615 0.4182 1.4595 1.1141
a9
mean 26.7277 26.4479 22.905 24.9246 24.574 24.5286
std. dev. 1.4583 0.0962 1.0479 0.468 1.4633 1.0347
a10
mean 60.7231 65.005 66.1638 52.9285 66.862 62.5743
std. dev. 1.5744 0.5594 0.0312 2.5265 0.5296 0.7717
a11
mean 61.11 64.345 65.8925 55.9162 66.965 62.7629
std. dev. 1.5199 0.8115 0.1947 0.8631 0.524 0.4349
a12
mean 19.1685 18.6579 19.5288 18.7523 18.84 18.1179
std. dev. 0.2159 0.0449 0.3056 0.2209 0.0819 0.3246
a13
mean 21.1854 20.535 20.9038 20.9115 20.685 20.3407
std. dev. 0.1433 0.071 0.1949 0.1113 0.0783 0.1172
a14
mean 17.0169 16.4729 17.2038 16.8992 16.634 16.1807
std. dev. 0.2785 0.064 0.2119 0.1203 0.126 0.2516
a15
mean 10.3785 10.1643 10.85 10.1738 9.765 9.4929
std. dev. 0.4533 0.392 0.5128 0.6548 0.2224 0.3111
a16
mean 14.1308 13.8893 14.5063 15.0431 14.253 13.8471
std. dev. 0.3811 0.1309 0.4907 0.6507 0.452 0.064
a17
mean 15.9108 16.7436 17.0863 16.0946 16.772 16.9236
std. dev. 0.4928 0.3323 0.3307 0.4258 0.3487 0.3038
a18
mean 29.7308 30.7293 30.9625 28.4931 31.118 30.285
std. dev. 0.5599 0.0758 0.0776 0.1048 0.5249 0.4079
a19
mean 2.5 2.6821 2.5575 2.7685 2.291 3.34
std. dev. 0.3076 0.399 0.1614 0.5731 0.1431 0.3487
a20
mean 5.63 4.9614 4.52 4.8262 5.136 5.385
std. dev. 0.7347 0.544 0.1989 0.7658 0.312 0.6348
a21
mean 5.8069 6.235 4.9013 5.3131 5.485 5.6679
std. dev. 0.8319 0.4368 0.1232 0.3849 0.3133 0.4095
a22
mean 7.4931 5.9421 4.2112 4.3538 7.191 4.605
std. dev. 1.3706 0.1848 0.5781 0.4594 0.8959 0.4618
a23
mean 6.5423 7.0343 6.18 5.34 8.413 5.3121
std. dev. 0.5213 0.751 0.6859 0.5581 0.5807 0.8649
a24
mean 10.9 8.8371 8.5125 6.5515 14.125 8.1264
std. dev. 0.9747 0.6736 1.3494 0.614 1.7997 1.673
a25
mean 3.9723 4.0336 4.3537 3.2085 4.552 4.2957
std. dev. 0.2284 0.1319 0.4002 0.0708 0.144 0.249
a26
mean 10.2208 10.8293 12.9838 10.8969 11.687 10.7771
std. dev. 0.1879 0.2439 0.7311 0.3519 0.2871 0.2287
a27
mean 5.6531 5.8821 8.575 5.4038 7.324 6.8407
std. dev. 0.4989 0.3965 0.4118 0.071 0.3789 0.3036
Time taken to build model (full training data) : 3.83 seconds
=== Model and evaluation on training set ===
Clustered Instances
0 13 ( 18%)
1 14 ( 19%)
2 8 ( 11%)
3 13 ( 18%)
4 10 ( 14%)
5 14 ( 19%)
Log likelihood: 2.84599
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