flag <- read.table("F:/Sztuczna inteligencja projekt/flag.data", header = F, sep = ",")
names(flag) <- c("name", "landmass", "zone", "area", "population", "language", "religion", "bars", "stripes", "colours", "red", "green", "blue", "gold", "white", "black", "orange", "mainhue", "circles", "crosses", "saltires", "quarters", "sunstars", "crescent", "triangle", "icon", "animate", "text", "topleft", "botright")
names(flag)
View(flag)

#k-sredniej
as.numeric(flag$name)
scale.flag <- scale(flag[ ,2:17])
flag.clusters <- kmeans(scale.flag, 15, iter.max = 10, algorithm = c("Hartigan-Wong""max.print") -- omitted 10 rows ]
> plot(flag.clusters$cluster)
> flag.clusters$size
 [1] 10 19 14 21 10 25 13 10 11 16  3 10 11 11 10
> flag.clusters$iter
[1] 3
> flag.clusters$centers
      landmass        zone          area  population   language     religion        bars     stripes    colours        red       green
1  -1.20550143  0.98500765 -0.1803590004 -0.15737407 -0.9838953 -0.868790030  0.04467882  0.06376859 -0.5106452 -0.2166051 -0.33791878
2   0.37715592 -0.04085190 -0.0735528657 -0.09050694  0.2790158  0.545840113  0.62759370 -0.39517411 -0.5187414  0.2591572  0.53518266
3   0.18349947 -0.10694369 -0.1974353059 -0.13810561  0.4134138  0.635212054  0.31984361 -0.02214187  0.1376295  0.3418186  0.34733812
4  -0.79769322  0.49362955 -0.2320266145 -0.18679474 -0.5331065 -0.647001704 -0.43685956 -0.05282418 -0.5399457  0.5163260 -0.84234399
5   0.53304937 -0.84947060  0.0001628835 -0.06165332  0.4746992 -0.092530890  0.14098649 -0.45169414 -0.7413870 -1.6824672  0.86128079
6   0.63607460 -0.77303400 -0.1200437785  0.02101461  0.9780573  0.761354164 -0.20572114 -0.11664337 -0.4491140  0.5163260 -0.13805218
7  -0.12076461  0.36763516 -0.2363967179 -0.19201427 -0.6692964 -0.353771946 -0.21461108 -0.23691801  2.3055886  0.5163260  0.75366031
8  -0.94793835  0.83213446  0.3535140881  0.01231272 -0.8408958 -0.723241441  2.54867839 -0.66647028 -0.4337312 -0.2166051  0.06181441
9   1.44622756 -0.09205344  0.0258159271 -0.18881953 -0.9032956 -0.621798485 -0.43685956 -0.54931966  0.3424004  0.2942257 -0.57412475
10  0.67792860 -0.87813432 -0.1336911684 -0.15519860  0.7785731 -0.001563022 -0.13589807  0.27317532  0.4123222  0.5163260  0.18673103
11  0.91939399 -0.92590719  5.0870506165  6.87520078  0.1887003  1.524675652 -0.43685956 -0.23691801 -0.6131971 -0.2980418 -0.27129658
12  0.08231397 -0.08510466 -0.2141694272 -0.16498822  0.5032991  1.217406409 -0.43685956  2.08266427  0.6430640  0.5163260  0.26168100
13 -1.12940325  0.88077593  0.6855673202  0.17606641 -0.8252959 -0.710009751 -0.43685956  1.28604006 -0.6365044 -1.7046772 -0.57412475
14 -1.36355151  1.22821499 -0.2920623860 -0.23925092 -1.0852949 -0.533587219 -0.43685956 -0.51026945  1.6009923  0.2942257  0.69775357
15  0.59744014 -0.77303400 -0.2217698638 -0.21067313  1.0752970  0.053017699 -0.43685956 -0.15100755 -0.8183010 -1.9267775 -0.93751856
          blue       gold      white       black      orange
1  -0.02056972  0.8612808 -1.7395367  0.29714738 -0.39238267
2  -0.49313198  0.7455685 -1.7395367 -0.60358062 -0.23828907
3  -0.87568238  0.7756237 -1.7395367  1.64823939  0.02587139
4   0.21696157 -0.8423440  0.5719025 -0.49635110 -0.39238267
5  -1.01820115 -0.5377854  0.1096146 -0.60358062  0.77872869
6  -0.22009601 -0.8575719  0.5719025 -0.51350782 -0.39238267
7   0.97706171  0.9074038  0.5719025 -0.08392985  2.53539574
8  -0.22009601 -0.7376520  0.5719025 -0.60358062  0.19317301
9   0.97706171  0.1526629  0.5719025 -0.39886971 -0.39238267
10 -0.89349722 -0.3129355  0.5719025  1.64823939 -0.20939652
11 -0.35311353  0.3949254 -0.9690570 -0.60358062  0.58354346
12 -0.02056972  0.6614142  0.5719025  0.07196538 -0.39238267
13  0.97706171 -0.3924278  0.5719025 -0.60358062 -0.12622100
14  0.61428665  1.0611474  0.5719025  1.03410666 -0.39238267
15  0.97706171 -0.5377854  0.3407586 -0.37839862 -0.09960483