5.4、聚类之EM聚类实例

EM实例

junjun

2016年2月9日

实例一、对鸢尾花数据做EM聚类分析

#1、加载数据data(iris)#2、聚类library(mclust)

## Package 'mclust' version 5.1

## Type 'citation("mclust")' for citing this R package in publications.

model_mclust <- Mclust(iris[, 1:4])#3、模型评估summary(model_mclust)

## ----------------------------------------------------## Gaussian finite mixture model fitted by EM algorithm ## ----------------------------------------------------## ## Mclust VEV (ellipsoidal, equal shape) model with 2 components:## ##  log.likelihood   n df       BIC       ICL##        -215.726 150 26 -561.7285 -561.7289## ## Clustering table:##   1   2 ##  50 100

#默认的情况下分成了两类#4、手动指定分类为3类model_mclust <- Mclust(iris[, 1:4], G=3)table(iris$Species, model_mclust$classification)

##             ##               1  2  3##   setosa     50  0  0##   versicolor  0 45  5##   virginica   0  0 50

summary(model_mclust)

## ----------------------------------------------------## Gaussian finite mixture model fitted by EM algorithm ## ----------------------------------------------------## ## Mclust VEV (ellipsoidal, equal shape) model with 3 components:## ##  log.likelihood   n df       BIC       ICL##       -186.0736 150 38 -562.5514 -566.4577## ## Clustering table:##  1  2  3 ## 50 45 55

#分成了三类#4、绘制聚类图library(fpc)plotcluster(iris[, 1:4], model_mclust$classification)

实例二、对geyser（美国黄石公园不老泉）数据做EM聚类分析

数据集geyser (package MASS)记录了它在1985年8月1日到8月15日300次喷发的299个间隔时间

#1、加载数据library(MASS)data(geyser)#2、聚类model_mclust <- Mclust(geyser)#3、模型评估summary(model_mclust)

## ----------------------------------------------------## Gaussian finite mixture model fitted by EM algorithm ## ----------------------------------------------------## ## Mclust VVI (diagonal, varying volume and shape) model with 4 components:## ##  log.likelihood   n df       BIC       ICL##        -1330.12 299 19 -2768.548 -2797.933## ## Clustering table:##  1  2  3  4 ## 17 90 94 98

#分成了4类#4、绘制聚类图:生成4幅图plot(model_mclust)

1

## [1] 1

2

## [1] 2

3

## [1] 3

4

## [1] 4

0

## [1] 0

#5、BIC基于模型的聚类geyser.bic <- mclustBIC(geyser)summary(geyser.bic)

## Best BIC values:##              VVI,4        VVE,4       VVV,4## BIC      -2768.548 -2771.952690 -2786.69691## BIC diff     0.000    -3.405101   -18.14932

#查看聚类结果的细节model_mclust$classification

##   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18 ##   2   3   4   2   2   3   4   2   3   4   3   4   3   4   2   3   4   3 ##  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36 ##   4   2   3   4   3   4   3   4   1   2   2   2   2   2   3   4   1   4 ##  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54 ##   3   4   3   4   3   4   3   4   3   4   3   4   3   4   1   4   3   4 ##  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72 ##   3   4   2   1   2   2   3   4   3   4   3   4   3   4   2   3   4   3 ##  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90 ##   4   2   2   3   4   2   2   2   2   3   4   1   2   3   4   3   4   3 ##  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 ##   4   3   4   3   4   3   4   3   4   3   4   3   4   3   4   3   4   3 ## 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 ##   4   1   4   2   3   4   3   4   3   4   3   4   3   4   2   2   3   4 ## 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 ##   2   2   2   2   2   2   1   4   2   2   2   2   3   4   2   2   2   2 ## 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 ##   2   2   3   4   1   4   3   4   3   4   3   4   3   2   2   2   2   2 ## 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 ##   2   3   4   3   4   3   4   1   4   1   2   3   4   1   4   3   4   2 ## 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 ##   3   4   3   4   2   2   2   3   4   3   4   3   4   3   4   1   2   3 ## 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 ##   4   3   4   3   4   2   3   2   2   1   2   2   2   3   4   3   4   3 ## 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 ##   4   1   2   2   3   4   2   2   2   2   2   2   3   4   3   4   3   4 ## 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 ##   2   2   2   3   4   2   3   4   1   2   3   4   1   3   4   3   4   2 ## 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 ##   2   3   4   3   4   2   2   2   2   3   4   2   2   3   4   3   4   1 ## 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 ##   4   2   3   4   3   4   2   2   2   2   2   2   2   3   2   3   4   3 ## 289 290 291 292 293 294 295 296 297 298 299 ##   4   3   4   3   4   3   4   3   4   2   3

#给出每个观测所在的类标签model_mclust$z

##             [,1]         [,2]          [,3]         [,4]## 1   1.712371e-02 9.828523e-01  4.154428e-56 2.395413e-05## 2   1.908623e-01 5.496943e-12  8.091377e-01 1.081293e-10## 3   4.911885e-06 9.963692e-03  3.344533e-58 9.900314e-01## 4   1.845617e-02 9.815206e-01  3.244363e-55 2.320322e-05## 5   1.102021e-02 9.880503e-01  1.828868e-55 9.295152e-04## 6   5.261056e-02 1.087875e-14  9.473894e-01 1.362518e-14## 7   6.989256e-06 1.825241e-02  1.008410e-79 9.817406e-01## 8   1.230018e-02 9.876994e-01  8.601935e-71 4.533391e-07## 9   6.553451e-02 3.515315e-14  9.344655e-01 3.121264e-14## 10  8.170111e-08 1.562112e-04 3.242660e-111 9.998437e-01## 11  3.860532e-02 3.793539e-17  9.613947e-01 1.468125e-17## 12  8.989668e-10 2.849915e-08 1.438044e-161 1.000000e+00## 13  1.486325e-01 8.621923e-20  8.513675e-01 7.742337e-22## 14  1.601203e-08 4.101444e-05 3.481180e-114 9.999590e-01## 15  1.358571e-02 9.864143e-01  1.540006e-76 2.467728e-08## 16  5.678594e-02 1.645633e-17  9.432141e-01 4.859862e-15## 17  2.760516e-06 4.512288e-03  2.567474e-98 9.954850e-01## 18  4.405887e-02 5.325086e-15  9.559411e-01 7.095595e-17## 19  1.271607e-04 6.123782e-02 1.898875e-101 9.386350e-01## 20  1.059042e-02 9.894010e-01  3.930266e-67 8.585709e-06## 21  3.042791e-02 1.815138e-16  9.695721e-01 3.222085e-18## 22  1.449813e-08 1.962650e-05 1.328421e-121 9.999804e-01## 23  3.909575e-02 4.144546e-15  9.609043e-01 1.137650e-17## 24  1.081799e-05 1.753770e-02  6.372394e-58 9.824515e-01## 25  5.109434e-02 8.624726e-15  9.489057e-01 2.454841e-15## 26  4.911885e-06 9.963692e-03  3.344533e-58 9.900314e-01## 27  9.999634e-01 3.657856e-05  6.845342e-10 1.241873e-11## 28  1.131787e-03 6.225411e-01  4.294530e-85 3.763271e-01## 29  9.329885e-03 9.901529e-01  5.094989e-59 5.172611e-04## 30  8.664914e-02 9.132763e-01  5.591223e-41 7.452542e-05## 31  2.788108e-01 7.199974e-01  4.637450e-32 1.191744e-03## 32  1.130841e-02 9.886895e-01  5.834570e-82 2.133147e-06## 33  4.355938e-01 4.365329e-11  5.644062e-01 2.809224e-11## 34  1.034665e-04 2.328839e-02 3.328805e-112 9.766081e-01## 35  9.999799e-01 8.411069e-07  1.923760e-05 1.259670e-08## 36  4.413944e-06 1.260288e-02  2.694606e-66 9.873927e-01## 37  1.934864e-01 3.982656e-12  8.065136e-01 1.103555e-17## 38  8.119853e-10 9.754007e-06 5.066399e-108 9.999902e-01## 39  2.688145e-02 1.782432e-17  9.731186e-01 6.030311e-20## 40  2.546083e-10 9.600512e-06  5.351581e-97 9.999904e-01## 41  4.014167e-01 4.508976e-11  5.985833e-01 1.239313e-17## 42  2.047060e-07 1.413377e-03  1.919387e-66 9.985864e-01## 43  3.640595e-02 3.647081e-15  9.635941e-01 4.488292e-18## 44  2.087966e-07 1.102223e-03  2.744737e-59 9.988976e-01## 45  5.203978e-02 9.693381e-15  9.479602e-01 5.811293e-15## 46  9.516096e-08 6.476579e-04  1.503661e-59 9.993522e-01## 47  3.220842e-02 1.245870e-16  9.677916e-01 5.837228e-18## 48  1.172433e-05 2.802541e-02  1.411329e-72 9.719629e-01## 49  6.553571e-02 6.596821e-14  9.344643e-01 1.547203e-17## 50  8.576818e-10 3.094477e-05  5.621461e-88 9.999691e-01## 51  9.991775e-01 6.103971e-08  8.224774e-04 9.570699e-11## 52  5.162328e-05 5.442228e-02  2.329855e-57 9.455261e-01## 53  2.249562e-02 1.581738e-18  9.775044e-01 1.788413e-22## 54  1.437598e-11 3.067372e-06  5.495979e-81 9.999969e-01## 55  1.840078e-01 2.584630e-12  8.159922e-01 4.474732e-17## 56  4.775574e-06 1.180618e-02  4.380298e-86 9.881890e-01## 57  4.583675e-02 9.541624e-01  6.579568e-49 8.656522e-07## 58  6.011036e-01 3.725396e-01  8.510565e-25 2.635675e-02## 59  5.003802e-02 9.485558e-01  5.814324e-42 1.406171e-03## 60  6.451900e-03 9.784161e-01  1.177121e-55 1.513199e-02## 61  8.102088e-04 2.479603e-17  9.991898e-01 1.413567e-28## 62  6.656613e-10 1.803876e-07 3.333923e-146 9.999998e-01## 63  5.261056e-02 1.087875e-14  9.473894e-01 1.362518e-14## 64  4.911885e-06 9.963692e-03  3.344533e-58 9.900314e-01## 65  4.979382e-02 7.662663e-15  9.502062e-01 1.027033e-15## 66  1.104099e-04 9.470874e-02  4.413160e-57 9.051808e-01## 67  4.624052e-02 6.021259e-15  9.537595e-01 1.746160e-16## 68  4.191823e-09 8.368417e-05  1.495252e-60 9.999163e-01## 69  4.034894e-01 5.965050e-01  1.578144e-32 5.579660e-06## 70  2.437649e-01 8.349853e-12  7.562351e-01 3.517368e-11## 71  2.198517e-05 3.545315e-02  1.399668e-86 9.645249e-01## 72  5.663984e-02 1.542470e-14  9.433602e-01 3.693414e-15## 73  1.864374e-07 1.352552e-03  2.129636e-67 9.986473e-01## 74  9.302494e-03 9.906597e-01  2.790531e-66 3.776261e-05## 75  3.519492e-03 9.867657e-01  1.958390e-74 9.714805e-03## 76  3.684821e-02 7.423608e-16  9.631518e-01 9.436657e-17## 77  1.423234e-05 1.636035e-02  1.392942e-96 9.836254e-01## 78  5.588744e-02 9.441042e-01  1.050173e-45 8.335602e-06## 79  5.557004e-03 9.783335e-01  1.901329e-57 1.610948e-02## 80  9.208083e-03 9.907191e-01  1.958142e-64 7.281499e-05## 81  1.304881e-02 9.868128e-01  4.895307e-95 1.383559e-04## 82  4.428700e-02 5.401481e-17  9.557130e-01 4.396963e-15## 83  2.373388e-05 3.093484e-02  1.218962e-57 9.690414e-01## 84  9.989103e-01 1.089619e-03  2.945780e-14 4.289712e-08## 85  2.091164e-03 6.998577e-01  5.121169e-56 2.980511e-01## 86  3.363951e-02 3.203667e-15  9.663605e-01 1.753208e-18## 87  2.090772e-08 2.671530e-04  6.914254e-86 9.997328e-01## 88  7.074505e-02 4.525855e-14  9.292550e-01 3.630057e-15## 89  4.769631e-09 1.213596e-04  2.722210e-74 9.998786e-01## 90  2.488235e-02 2.061851e-16  9.751177e-01 2.746721e-19## 91  2.208021e-09 3.022075e-05 8.710379e-101 9.999698e-01## 92  6.611653e-02 2.591548e-18  9.338835e-01 1.503507e-18## 93  1.427578e-06 5.733029e-03  2.688308e-80 9.942655e-01## 94  5.361564e-02 6.577451e-18  9.463844e-01 5.531434e-18## 95  5.540041e-10 1.581969e-05  9.389071e-97 9.999842e-01## 96  1.485593e-02 2.320895e-17  9.851441e-01 8.275824e-22## 97  1.990107e-09 5.548167e-05  1.170162e-86 9.999445e-01## 98  1.303771e-01 1.369786e-19  8.696229e-01 2.275022e-21## 99  8.320185e-06 1.173692e-03 2.187750e-124 9.988180e-01## 100 1.503777e-02 4.985916e-18  9.849622e-01 1.330165e-22## 101 6.788114e-08 2.017617e-05 2.741315e-131 9.999798e-01## 102 1.197112e-01 1.210065e-19  8.802888e-01 8.791932e-22## 103 8.862344e-09 1.814522e-04  4.865949e-76 9.998185e-01## 104 5.109434e-02 8.624726e-15  9.489057e-01 2.454841e-15## 105 1.081799e-05 1.753770e-02  6.372394e-58 9.824515e-01## 106 4.624052e-02 6.021259e-15  9.537595e-01 1.746160e-16## 107 7.100517e-09 1.053866e-04  1.472903e-91 9.998946e-01## 108 3.084189e-02 2.809068e-15  9.691581e-01 6.780234e-19## 109 9.516096e-08 6.476579e-04  1.503661e-59 9.993522e-01## 110 9.995408e-01 4.589818e-04  1.517407e-12 2.339779e-07## 111 1.895828e-03 4.675666e-01 2.134447e-100 5.305376e-01## 112 1.854713e-02 9.806567e-01  3.209734e-50 7.961402e-04## 113 4.213133e-02 2.033372e-15  9.578687e-01 4.321108e-15## 114 1.023993e-07 8.594455e-04  9.253959e-66 9.991405e-01## 115 4.568953e-02 1.741316e-17  9.543105e-01 2.119738e-17## 116 5.148284e-08 2.581066e-04 9.881763e-100 9.997418e-01## 117 2.688145e-02 1.782432e-17  9.731186e-01 6.030311e-20## 118 9.804871e-09 7.126576e-05 1.430486e-102 9.999287e-01## 119 1.079312e-01 3.033847e-13  8.920688e-01 2.573437e-16## 120 4.809594e-06 3.980767e-03 3.456688e-106 9.960144e-01## 121 1.503777e-02 4.985916e-18  9.849622e-01 1.330165e-22## 122 2.480173e-07 1.535529e-03  1.495232e-64 9.984642e-01## 123 7.556328e-03 9.872337e-01  3.425860e-94 5.209985e-03## 124 1.845617e-02 9.815206e-01  3.244363e-55 2.320322e-05## 125 4.816491e-02 6.797757e-15  9.518351e-01 4.255428e-16## 126 2.373388e-05 3.093484e-02  1.218962e-57 9.690414e-01## 127 2.807414e-02 9.719256e-01  6.845003e-55 2.109662e-07## 128 7.425926e-03 9.923960e-01  2.433873e-67 1.780285e-04## 129 3.839483e-03 9.759603e-01  5.959065e-63 2.020020e-02## 130 1.925266e-02 9.805523e-01  7.531002e-52 1.950134e-04## 131 5.553722e-02 9.441276e-01  1.124851e-42 3.352060e-04## 132 1.463722e-02 9.853628e-01  2.502679e-78 9.266733e-10## 133 9.976550e-01 4.778245e-08  2.344903e-03 3.824847e-09## 134 7.365612e-09 1.564695e-04  2.139914e-69 9.998435e-01## 135 7.206807e-02 9.279302e-01  8.934139e-45 1.726254e-06## 136 9.163229e-03 9.908247e-01  5.510843e-73 1.208648e-05## 137 1.541738e-02 9.782479e-01  1.176280e-48 6.334694e-03## 138 9.442055e-03 9.875019e-01  2.387294e-96 3.056089e-03## 139 1.011455e-01 2.698995e-18  8.988545e-01 4.317921e-16## 140 9.668025e-05 1.268354e-02 2.137182e-118 9.872198e-01## 141 7.502862e-03 9.923977e-01  3.423572e-70 9.945091e-05## 142 3.618023e-03 9.382483e-01  4.695242e-90 5.813370e-02## 143 1.529560e-02 9.846003e-01  2.566650e-55 1.041122e-04## 144 1.379782e-02 9.859844e-01  2.288819e-55 2.178098e-04## 145 1.685275e-02 9.830979e-01  2.883144e-55 4.935361e-05## 146 1.102021e-02 9.880503e-01  1.828868e-55 9.295152e-04## 147 4.762466e-02 5.480178e-15  9.523753e-01 3.911963e-15## 148 7.780449e-05 7.039065e-02  1.334131e-92 9.295315e-01## 149 1.000000e+00 1.074572e-31  5.754293e-13 1.745203e-25## 150 2.999984e-10 2.081499e-06 3.748016e-119 9.999979e-01## 151 4.440713e-02 1.217471e-18  9.555929e-01 5.878822e-21## 152 2.932005e-08 2.376509e-04  2.381541e-94 9.997623e-01## 153 7.619621e-02 8.308081e-19  9.238038e-01 6.840244e-20## 154 1.832813e-09 1.817827e-05 1.369082e-106 9.999818e-01## 155 9.211187e-03 5.070508e-18  9.907888e-01 8.070693e-24## 156 1.660158e-09 4.391452e-05  6.923988e-90 9.999561e-01## 157 3.702118e-02 1.100283e-16  9.629788e-01 6.247904e-17## 158 6.374299e-03 9.931920e-01  2.690877e-81 4.336577e-04## 159 8.398879e-03 9.915559e-01  3.846441e-81 4.520757e-05## 160 7.473132e-03 9.848690e-01  1.316000e-55 7.657867e-03## 161 1.205413e-02 9.615087e-01 2.385203e-104 2.643720e-02## 162 1.529560e-02 9.846003e-01  2.566650e-55 1.041122e-04## 163 2.948829e-02 9.705116e-01  7.803410e-55 9.367654e-08## 164 4.392913e-02 2.598678e-14  9.560709e-01 8.796410e-12## 165 1.010577e-06 3.267612e-03  9.412580e-59 9.967314e-01## 166 3.367279e-02 5.809464e-16  9.663272e-01 1.571896e-17## 167 5.817326e-10 1.730603e-05  1.233014e-95 9.999827e-01## 168 4.624052e-02 6.021259e-15  9.537595e-01 1.746160e-16## 169 4.442890e-05 1.287245e-02  6.365798e-43 9.870831e-01## 170 9.999159e-01 8.410149e-05  8.340990e-11 6.033089e-10## 171 1.809080e-07 2.747876e-04 6.163162e-111 9.997250e-01## 172 6.755478e-01 3.244518e-01  5.475697e-29 3.286932e-07## 173 3.450200e-03 9.847295e-01  1.889457e-77 1.182032e-02## 174 4.775967e-02 2.203220e-17  9.522403e-01 1.199652e-16## 175 6.021532e-07 3.023479e-03  1.263747e-81 9.969759e-01## 176 9.985978e-01 4.570872e-08  1.402156e-03 1.722379e-10## 177 3.903999e-07 1.775253e-03  4.100402e-89 9.982244e-01## 178 5.935014e-02 8.440995e-14  9.406499e-01 1.406201e-18## 179 3.834376e-05 6.612729e-02  4.489552e-77 9.338344e-01## 180 1.214806e-02 9.878517e-01  1.049404e-75 2.758471e-07## 181 5.221336e-02 1.429362e-17  9.477866e-01 9.457430e-17## 182 3.131652e-09 9.781693e-06 2.149491e-118 9.999902e-01## 183 7.079404e-03 2.385809e-18  9.929206e-01 7.658214e-25## 184 4.191823e-09 8.368417e-05  1.495252e-60 9.999163e-01## 185 1.529560e-02 9.846003e-01  2.566650e-55 1.041122e-04## 186 1.685275e-02 9.830979e-01  2.883144e-55 4.935361e-05## 187 3.102876e-03 5.963520e-01  3.574795e-49 4.005452e-01## 188 3.186398e-02 4.176905e-17  9.681360e-01 1.297010e-18## 189 9.331343e-10 1.479800e-05 2.420564e-103 9.999852e-01## 190 4.888012e-02 9.531201e-15  9.511199e-01 1.068423e-16## 191 5.068201e-06 1.275095e-02  5.212531e-85 9.872440e-01## 192 3.702118e-02 1.100283e-16  9.629788e-01 6.247904e-17## 193 7.365612e-09 1.564695e-04  2.139914e-69 9.998435e-01## 194 2.245692e-02 1.081155e-16  9.775431e-01 7.628236e-20## 195 4.769631e-09 1.213596e-04  2.722210e-74 9.998786e-01## 196 9.621958e-01 3.780413e-02  1.005648e-21 5.930414e-08## 197 4.715666e-03 9.920567e-01  1.546939e-67 3.227612e-03## 198 3.868975e-02 2.859417e-16  9.613103e-01 2.609301e-15## 199 9.579221e-05 1.116814e-02 1.182834e-119 9.887361e-01## 200 2.138634e-02 2.176777e-17  9.786137e-01 1.175315e-20## 201 9.120580e-09 1.378878e-04  2.623215e-60 9.998621e-01## 202 2.612051e-02 9.048888e-16  9.738795e-01 3.101833e-19## 203 1.774590e-09 1.633781e-05 9.068643e-108 9.999837e-01## 204 1.529560e-02 9.846003e-01  2.566650e-55 1.041122e-04## 205 3.909575e-02 4.144546e-15  9.609043e-01 1.137650e-17## 206 1.384796e-03 5.527917e-01  3.682357e-56 4.458235e-01## 207 1.102021e-02 9.880503e-01  1.828868e-55 9.295152e-04## 208 9.723377e-01 5.162143e-09  2.766230e-02 1.863156e-10## 209 1.633526e-03 8.232313e-01  9.581736e-80 1.751352e-01## 210 1.693872e-02 9.830613e-01  1.482685e-66 2.475816e-09## 211 1.264023e-03 7.479955e-01  8.774216e-77 2.507405e-01## 212 3.363951e-02 3.203667e-15  9.663605e-01 1.753208e-18## 213 1.190201e-05 2.471212e-02  1.196805e-83 9.752760e-01## 214 6.032591e-02 4.362387e-18  9.396741e-01 4.473112e-18## 215 8.456814e-08 6.280017e-04  1.405545e-88 9.993719e-01## 216 9.852420e-02 5.198921e-20  9.014758e-01 1.575485e-23## 217 9.804871e-09 7.126576e-05 1.430486e-102 9.999287e-01## 218 9.999099e-01 1.989830e-07  8.994551e-05 8.765469e-12## 219 3.750359e-02 9.403544e-01  1.503428e-40 2.214201e-02## 220 4.002060e-03 9.891405e-01  1.508971e-68 6.857461e-03## 221 1.347835e-02 1.533644e-16  9.865216e-01 1.109707e-21## 222 2.990476e-09 8.459797e-05  2.293825e-80 9.999154e-01## 223 2.948829e-02 9.705116e-01  7.803410e-55 9.367654e-08## 224 1.379782e-02 9.859844e-01  2.288819e-55 2.178098e-04## 225 9.753465e-03 9.883512e-01  1.638105e-55 1.895366e-03## 226 3.594411e-03 9.834327e-01  1.236313e-67 1.297289e-02## 227 2.173875e-02 9.782562e-01  4.130437e-55 5.003727e-06## 228 5.838055e-03 9.916170e-01  8.341094e-63 2.544937e-03## 229 3.645968e-02 1.795673e-16  9.635403e-01 8.462596e-17## 230 8.576818e-10 3.094477e-05  5.621461e-88 9.999691e-01## 231 1.804354e-02 3.477867e-16  9.819565e-01 1.163530e-20## 232 5.930838e-08 6.499846e-04  1.522792e-71 9.993500e-01## 233 6.059190e-02 1.069838e-18  9.394081e-01 3.222403e-20## 234 9.126161e-10 3.339636e-05  6.603299e-87 9.999666e-01## 235 7.699320e-03 9.922067e-01  3.216449e-69 9.402496e-05## 236 3.740057e-02 9.625994e-01  5.979464e-53 1.695260e-08## 237 1.211703e-02 9.878827e-01  1.025071e-76 2.951992e-07## 238 4.486837e-02 3.940978e-15  9.551316e-01 1.452512e-14## 239 2.431649e-06 7.671629e-03  3.141838e-84 9.923259e-01## 240 8.224367e-03 9.917288e-01  3.856089e-70 4.679966e-05## 241 2.072124e-02 1.579894e-16  9.792788e-01 4.060919e-20## 242 4.693963e-11 5.513497e-06  9.794108e-86 9.999945e-01## 243 9.993843e-01 6.157005e-04  8.252734e-14 1.379761e-12## 244 7.473132e-03 9.848690e-01  1.316000e-55 7.657867e-03## 245 4.806399e-02 2.109125e-14  9.519360e-01 1.849399e-12## 246 4.589838e-07 1.890608e-03  5.058916e-59 9.981089e-01## 247 9.155516e-01 8.444274e-02  5.896656e-23 5.675475e-06## 248 4.170960e-02 8.515251e-17  9.582904e-01 5.723690e-15## 249 1.592563e-04 1.148708e-01  1.964385e-93 8.849699e-01## 250 4.174485e-02 5.413838e-17  9.582552e-01 2.006521e-16## 251 2.871357e-07 1.052438e-03  9.069106e-96 9.989473e-01## 252 1.583202e-02 9.841672e-01  2.468706e-90 8.280948e-07## 253 3.373687e-03 9.822101e-01  1.450731e-69 1.441626e-02## 254 3.182232e-02 5.126446e-16  9.681777e-01 6.321435e-18## 255 6.022516e-11 4.664757e-07 1.246921e-124 9.999995e-01## 256 3.534486e-02 1.517188e-15  9.646551e-01 1.308440e-17## 257 9.308866e-07 4.465344e-03  1.924791e-75 9.955337e-01## 258 1.494656e-02 9.850533e-01  6.749564e-66 1.533128e-07## 259 1.116784e-03 5.832952e-01  3.117560e-87 4.155880e-01## 260 1.153471e-02 9.884646e-01  8.668413e-78 7.120164e-07## 261 6.080502e-03 9.850671e-01  4.680817e-92 8.852391e-03## 262 5.103028e-02 1.701332e-14  9.489697e-01 3.740050e-13## 263 2.087966e-07 1.102223e-03  2.744737e-59 9.988976e-01## 264 2.173875e-02 9.782562e-01  4.130437e-55 5.003727e-06## 265 5.294032e-02 9.470597e-01  4.265000e-50 5.242067e-10## 266 5.261056e-02 1.087875e-14  9.473894e-01 1.362518e-14## 267 3.836632e-08 3.629503e-04  7.545862e-89 9.996370e-01## 268 1.059019e-02 2.073642e-18  9.894098e-01 4.848999e-24## 269 4.191823e-09 8.368417e-05  1.495252e-60 9.999163e-01## 270 9.999929e-01 7.075503e-06  5.080794e-08 1.674725e-12## 271 2.623841e-05 1.830247e-02 4.729213e-102 9.816713e-01## 272 2.928510e-02 9.707139e-01  3.108797e-53 9.831744e-07## 273 4.228050e-02 2.278856e-15  9.577195e-01 1.003477e-14## 274 8.593367e-06 1.982998e-03 2.548728e-119 9.980084e-01## 275 3.371679e-02 4.733400e-17  9.662832e-01 3.225154e-18## 276 7.650043e-10 7.838944e-06 2.131911e-110 9.999922e-01## 277 2.657071e-02 9.734288e-01  6.015880e-55 4.712810e-07## 278 1.529560e-02 9.846003e-01  2.566650e-55 1.041122e-04## 279 1.529560e-02 9.846003e-01  2.566650e-55 1.041122e-04## 280 2.009059e-02 9.798986e-01  3.657372e-55 1.081939e-05## 281 1.529560e-02 9.846003e-01  2.566650e-55 1.041122e-04## 282 1.237003e-02 9.871781e-01  2.044415e-55 4.518714e-04## 283 9.753465e-03 9.883512e-01  1.638105e-55 1.895366e-03## 284 4.816491e-02 6.797757e-15  9.518351e-01 4.255428e-16## 285 2.091164e-03 6.998577e-01  5.121169e-56 2.980511e-01## 286 3.182232e-02 5.126446e-16  9.681777e-01 6.321435e-18## 287 6.784500e-10 3.295928e-05  7.861014e-80 9.999670e-01## 288 5.135119e-02 1.419417e-19  9.486488e-01 2.290029e-23## 289 7.881744e-11 2.180057e-06 9.381515e-109 9.999978e-01## 290 2.683201e-02 5.964256e-16  9.731680e-01 5.538056e-19## 291 2.120256e-09 2.739220e-05 6.186260e-102 9.999726e-01## 292 3.985077e-02 1.059596e-15  9.601492e-01 1.289921e-15## 293 5.232289e-08 5.312982e-04  1.687280e-83 9.994686e-01## 294 1.140570e-01 4.538949e-13  8.859430e-01 6.534535e-17## 295 5.673011e-08 5.467823e-04  3.840710e-64 9.994532e-01## 296 6.460446e-02 4.547448e-14  9.353955e-01 6.316094e-17## 297 1.081799e-05 1.753770e-02  6.372394e-58 9.824515e-01## 298 3.079230e-02 9.692077e-01  8.913354e-55 4.126135e-08## 299 5.109434e-02 8.624726e-15  9.489057e-01 2.454841e-15

#6、手动指定聚类个数model_mclust2 <- Mclust(geyser, G=2)summary(model_mclust2, parameters=T)

## ----------------------------------------------------## Gaussian finite mixture model fitted by EM algorithm ## ----------------------------------------------------## ## Mclust VVE (ellipsoidal, equal orientation) model with 2 components:## ##  log.likelihood   n df       BIC       ICL##       -1404.049 299 10 -2865.102 -2869.098## ## Clustering table:##   1   2 ## 104 195 ## ## Mixing probabilities:##         1         2 ## 0.3466742 0.6533258 ## ## Means:##              [,1]      [,2]## waiting  83.17384 66.552028## duration  1.96897  4.252431## ## Variances:## [,,1]##             waiting    duration## waiting  43.7862624 -0.56757440## duration -0.5675744  0.07615526## [,,2]##             waiting   duration## waiting  175.317237 -2.2743659## duration  -2.274366  0.1635021