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私はクラスタリングにk-meansアルゴリズムを使用していますが、結果に基づいてkの最良最適値を決める方法がわかりません。 は元のために、私は、K = 10のためのデータセットでのk-means法を適用した:wekaのk-meansアルゴリズムで最適な 'k'を決定する
kMeans
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Number of iterations: 16
Within cluster sum of squared errors: 38.47923197081721
Missing values globally replaced with mean/mode
Cluster centroids:
Cluster#
Attribute Full Data 0 1 2 3 4 5 6 7 8 9
(214) (16) (9) (13) (23) (46) (12) (11) (40) (15) (29)
==============================================================================================================================================================================================================================================================
RI 1.5184 1.5181 1.5175 1.5189 1.5178 1.5172 1.519 1.5255 1.5175 1.5222 1.5171
Na 13.4079 12.9988 14.6467 12.8277 13.2148 13.1896 13.63 12.6318 13.0518 13.9107 14.4421
Mg 2.6845 3.4894 1.3056 0.7738 3.4261 3.4987 3.4917 0.2145 3.4958 3.8273 0.5383
Al 1.4449 1.1844 1.3667 2.0338 1.3552 1.4898 1.3308 1.1891 1.2617 0.716 2.1228
Si 72.6509 72.785 73.2067 72.3662 72.6526 72.6989 72.07 72.0709 72.9532 71.7467 72.9659
K 0.4971 0.4794 0 1.47 0.527 0.59 0.4108 0.2345 0.547 0.1007 0.3252
Ca 8.957 8.8069 9.3567 10.1238 8.5648 8.3041 8.87 13.1291 8.5035 9.5887 8.4914
Ba 0.175 0.015 0 0.1877 0.023 0.003 0.0667 0.2864 0 0 1.04
Fe 0.057 0.2238 0 0.0608 0.2013 0.0104 0.0167 0.1109 0.011 0.0313 0.0134
Type build wind non-float build wind float tableware containers build wind non-float build wind non-float build wind float build wind non-float build wind float build wind float headlamps
非常に便利です.... – Aquarius24