02-21-24
oyewole oyemakinde
2024-02-22
set.seed(1234)
randDat <-matrix(rnorm(50), nrow = 5)
randDat## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -1.2070657 0.5060559 -0.47719270 -0.1102855 0.1340882 -1.4482049
## [2,] 0.2774292 -0.5747400 -0.99838644 -0.5110095 -0.4906859 0.5747557
## [3,] 1.0844412 -0.5466319 -0.77625389 -0.9111954 -0.4405479 -1.0236557
## [4,] -2.3456977 -0.5644520 0.06445882 -0.8371717 0.4595894 -0.0151383
## [5,] 0.4291247 -0.8900378 0.95949406 2.4158352 -0.6937202 -0.9359486
## [,7] [,8] [,9] [,10]
## [1,] 1.1022975 -1.1676193 1.4494963 -0.9685143
## [2,] -0.4755931 -2.1800396 -1.0686427 -1.1073182
## [3,] -0.7094400 -1.3409932 -0.8553646 -1.2519859
## [4,] -0.5012581 -0.2942939 -0.2806230 -0.5238281
## [5,] -1.6290935 -0.4658975 -0.9943401 -0.4968500dist(randDat) ## 1 2 3 4
## 2 4.261667
## 3 4.038030 2.060117
## 4 3.456732 3.726399 4.037978
## 5 5.307253 4.415046 4.111230 4.814393# Euclidean distance (default)>
dist(randDat, method = "manhattan")## 1 2 3 4
## 2 11.382197
## 3 10.016795 4.536827
## 4 9.887932 8.845512 8.829131
## 5 14.683770 10.617871 9.091241 11.362705dist(randDat, method = "minkowski", p= 4)## 1 2 3 4
## 2 2.899494
## 3 2.875467 1.653824
## 4 2.208297 2.814135 3.453336
## 5 3.488531 3.192217 3.398721 3.643788d<-dist(scale(iris[, -5]))
h<-hclust(d)
plot (h)
plot (h, hang = -0.1, labels = iris[["species"]],cex=0.5)
#cex is to reduce front
c<-cutree(h,3)
c## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 2 1 1 1 1 1 1 1 1 3 3 3 2 3 2 3 2 3 2 2 3 2 3 3 3 3 2 2 2 3 3 3 3
## [75] 3 3 3 3 3 2 2 2 2 3 3 3 3 2 3 2 2 3 2 2 2 3 3 3 2 2 3 3 3 3 3 3 2 3 3 3 3
## [112] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [149] 3 3(cm<- table(c, iris$Species))##
## c setosa versicolor virginica
## 1 49 0 0
## 2 1 21 2
## 3 0 29 48Error<-100*(1-sum(diag(cm))/sum(cm))
Error## [1] 21.33333library(cluster)
silhouette(c, d)## cluster neighbor sil_width
## [1,] 1 2 0.74381061
## [2,] 1 2 0.52251215
## [3,] 1 2 0.66073019
## [4,] 1 2 0.58819251
## [5,] 1 2 0.74002653
## [6,] 1 2 0.63964608
## [7,] 1 2 0.69599504
## [8,] 1 2 0.73518521
## [9,] 1 2 0.41172906
## [10,] 1 2 0.60436547
## [11,] 1 2 0.69419410
## [12,] 1 2 0.72329651
## [13,] 1 2 0.53223683
## [14,] 1 2 0.49535416
## [15,] 1 3 0.57762368
## [16,] 1 3 0.46169664
## [17,] 1 2 0.64765800
## [18,] 1 2 0.73950603
## [19,] 1 2 0.61735113
## [20,] 1 2 0.69713933
## [21,] 1 2 0.67059935
## [22,] 1 2 0.70888740
## [23,] 1 2 0.70027595
## [24,] 1 2 0.63966308
## [25,] 1 2 0.70331638
## [26,] 1 2 0.49852786
## [27,] 1 2 0.71129988
## [28,] 1 2 0.73277924
## [29,] 1 2 0.72073892
## [30,] 1 2 0.65104081
## [31,] 1 2 0.59701883
## [32,] 1 2 0.66005618
## [33,] 1 2 0.61396099
## [34,] 1 3 0.56172259
## [35,] 1 2 0.60158592
## [36,] 1 2 0.66343730
## [37,] 1 2 0.67907313
## [38,] 1 2 0.73344514
## [39,] 1 2 0.49893006
## [40,] 1 2 0.73032162
## [41,] 1 2 0.74223204
## [42,] 2 1 0.11505796
## [43,] 1 2 0.61310051
## [44,] 1 2 0.67752577
## [45,] 1 2 0.67266764
## [46,] 1 2 0.51096571
## [47,] 1 2 0.69769140
## [48,] 1 2 0.64717198
## [49,] 1 2 0.70772044
## [50,] 1 2 0.70821522
## [51,] 3 2 0.44810916
## [52,] 3 2 0.42551507
## [53,] 3 2 0.48113585
## [54,] 2 3 0.60056798
## [55,] 3 2 0.28909599
## [56,] 2 3 0.25532064
## [57,] 3 2 0.44163580
## [58,] 2 3 0.58862119
## [59,] 3 2 0.31165524
## [60,] 2 3 0.47925063
## [61,] 2 3 0.53533494
## [62,] 3 2 0.13798259
## [63,] 2 3 0.50489380
## [64,] 3 2 0.19049376
## [65,] 3 2 -0.29889737
## [66,] 3 2 0.41447292
## [67,] 3 2 0.01586865
## [68,] 2 3 0.44246194
## [69,] 2 3 0.34101152
## [70,] 2 3 0.61849466
## [71,] 3 2 0.37763392
## [72,] 3 2 -0.13265959
## [73,] 3 2 -0.10917695
## [74,] 3 2 -0.04975816
## [75,] 3 2 0.21535096
## [76,] 3 2 0.37677150
## [77,] 3 2 0.33076400
## [78,] 3 2 0.51039252
## [79,] 3 2 0.14817057
## [80,] 2 3 0.55591487
## [81,] 2 3 0.64017720
## [82,] 2 3 0.63633810
## [83,] 2 3 0.43828812
## [84,] 3 2 0.05444339
## [85,] 3 2 -0.08567781
## [86,] 3 2 0.35396959
## [87,] 3 2 0.46838922
## [88,] 2 3 0.33877191
## [89,] 3 2 -0.13113484
## [90,] 2 3 0.60086588
## [91,] 2 3 0.54141464
## [92,] 3 2 0.25946590
## [93,] 2 3 0.51105620
## [94,] 2 3 0.59963069
## [95,] 2 3 0.45426587
## [96,] 3 2 -0.11025116
## [97,] 3 2 -0.20236125
## [98,] 3 2 0.12488131
## [99,] 2 3 0.58669598
## [100,] 2 3 0.31751136
## [101,] 3 2 0.48030393
## [102,] 3 2 0.07781886
## [103,] 3 2 0.52818285
## [104,] 3 2 0.45479299
## [105,] 3 2 0.52765484
## [106,] 3 2 0.46070084
## [107,] 2 3 0.42297967
## [108,] 3 2 0.46821593
## [109,] 3 2 0.24408754
## [110,] 3 2 0.45830809
## [111,] 3 2 0.54054151
## [112,] 3 2 0.34990684
## [113,] 3 2 0.54864397
## [114,] 3 2 -0.14954119
## [115,] 3 2 0.24982270
## [116,] 3 2 0.52152482
## [117,] 3 2 0.52530621
## [118,] 3 2 0.40527772
## [119,] 3 2 0.35156138
## [120,] 2 3 0.35090417
## [121,] 3 2 0.54433023
## [122,] 3 2 0.07700160
## [123,] 3 2 0.40831945
## [124,] 3 2 0.26315229
## [125,] 3 2 0.54408598
## [126,] 3 2 0.51663483
## [127,] 3 2 0.30994750
## [128,] 3 2 0.41268004
## [129,] 3 2 0.44434621
## [130,] 3 2 0.48294810
## [131,] 3 2 0.44165933
## [132,] 3 2 0.39458828
## [133,] 3 2 0.44191565
## [134,] 3 2 0.28181352
## [135,] 3 2 -0.02944153
## [136,] 3 2 0.45534598
## [137,] 3 2 0.48077840
## [138,] 3 2 0.52641092
## [139,] 3 2 0.36551707
## [140,] 3 2 0.55583283
## [141,] 3 2 0.53460231
## [142,] 3 2 0.53372987
## [143,] 3 2 0.07781886
## [144,] 3 2 0.54363634
## [145,] 3 2 0.51463269
## [146,] 3 2 0.52659597
## [147,] 3 2 0.08512800
## [148,] 3 2 0.53286756
## [149,] 3 2 0.47143716
## [150,] 3 2 0.34701606
## attr(,"Ordered")
## [1] FALSE
## attr(,"call")
## silhouette.default(x = c, dist = d)
## attr(,"class")
## [1] "silhouette"plot(silhouette(c, d))
set.seed(1234)
d<-dist(scale(iris[,-5]))
methds<-c('complete', 'single', 'average')
avgs<-matrix(NA,ncol=3,nrow = 5, dimnames = list(2:6,methds))
for (k in 2:6){
for (m in seq_along(methds)){
h<-hclust(d,meth=methds[m])
c<- cutree(h,k)
s<-silhouette(c,d)
avgs[k-1,m]=mean(s[,3])
}
}
avgs## complete single average
## 2 0.4408121 0.5817500 0.5817500
## 3 0.4496185 0.5046456 0.4802669
## 4 0.4106071 0.4067465 0.4067465
## 5 0.3520630 0.3424089 0.3746013
## 6 0.3106991 0.2018867 0.3248248seeds_dataset <- read.delim("/Users/makindeoye/Downloads/seeds_dataset.txt")
View(seeds_dataset)
sdf<- seeds_dataset[,-8]
View (sdf)
sdf## X15.26 X14.84 X0.871 X5.763 X3.312 X2.221 X5.22
## 1 14.880 14.57 0.8811 5.5540 3.333 1.0180 4.956
## 2 14.290 14.09 0.9050 5.2910 3.337 2.6990 4.825
## 3 13.840 13.94 0.8955 5.3240 3.379 2.2590 4.805
## 4 16.140 14.99 0.9034 5.6580 3.562 1.3550 5.175
## 5 14.380 14.21 0.8951 5.3860 3.312 2.4620 4.956
## 6 14.690 14.49 0.8799 5.5630 3.259 3.5860 5.219
## 7 14.110 14.10 0.8911 5.4200 3.302 2.7000 NA
## 8 NA 1.00 NA NA NA NA NA
## 9 16.630 15.46 0.8747 6.0530 3.465 2.0400 5.877
## 10 16.440 15.25 0.8880 5.8840 3.505 1.9690 5.533
## 11 15.260 14.85 0.8696 5.7140 3.242 4.5430 5.314
## 12 14.030 14.16 0.8796 5.4380 3.201 1.7170 5.001
## 13 13.890 14.02 0.8880 5.4390 3.199 3.9860 4.738
## 14 13.780 14.06 0.8759 5.4790 3.156 3.1360 4.872
## 15 13.740 14.05 0.8744 5.4820 3.114 2.9320 4.825
## 16 14.590 14.28 0.8993 5.3510 3.333 4.1850 4.781
## 17 13.990 13.83 0.9183 5.1190 3.383 5.2340 4.781
## 18 15.690 14.75 0.9058 5.5270 3.514 1.5990 5.046
## 19 14.700 14.21 0.9153 5.2050 3.466 1.7670 4.649
## 20 12.720 13.57 0.8686 5.2260 3.049 4.1020 4.914
## 21 14.160 14.40 0.8584 5.6580 3.129 3.0720 5.176
## 22 14.110 14.26 0.8722 5.5200 3.168 2.6880 5.219
## 23 15.880 14.90 0.8988 5.6180 3.507 0.7651 5.091
## 24 12.080 13.23 0.8664 5.0990 2.936 1.4150 4.961
## 25 15.010 14.76 0.8657 5.7890 3.245 1.7910 5.001
## 26 16.190 15.16 0.8849 5.8330 3.421 0.9030 5.307
## 27 13.020 13.76 0.8641 5.3950 3.026 3.3730 4.825
## 28 12.740 13.67 0.8564 5.3950 2.956 2.5040 4.869
## 29 14.110 14.18 0.8820 5.5410 3.221 2.7540 5.038
## 30 13.450 14.02 0.8604 5.5160 3.065 3.5310 5.097
## 31 13.160 13.82 0.8662 5.4540 2.975 0.8551 5.056
## 32 15.490 14.94 0.8724 5.7570 3.371 3.4120 5.228
## 33 14.090 14.41 0.8529 5.7170 3.186 3.9200 5.299
## 34 13.940 14.17 0.8728 5.5850 3.150 2.1240 5.012
## 35 15.050 14.68 0.8779 5.7120 3.328 2.1290 5.360
## 36 16.120 15.00 NA 0.9000 NA 5.7090 3.485
## 37 5.443 1.00 NA NA NA NA NA
## 38 16.200 15.27 0.8734 5.8260 3.464 2.8230 5.527
## 39 17.080 15.38 0.9079 5.8320 3.683 2.9560 5.484
## 40 14.800 14.52 0.8823 5.6560 3.288 3.1120 5.309
## 41 14.280 14.17 0.8944 5.3970 3.298 6.6850 5.001
## 42 13.540 13.85 0.8871 5.3480 3.156 2.5870 5.178
## 43 13.500 13.85 0.8852 5.3510 3.158 2.2490 5.176
## 44 13.160 13.55 0.9009 5.1380 3.201 2.4610 4.783
## 45 15.500 14.86 0.8820 5.8770 3.396 4.7110 5.528
## 46 15.110 14.54 0.8986 5.5790 3.462 3.1280 5.180
## 47 13.800 14.04 0.8794 5.3760 3.155 1.5600 4.961
## 48 15.360 14.76 0.8861 5.7010 3.393 1.3670 5.132
## 49 14.990 14.56 0.8883 5.5700 3.377 2.9580 5.175
## 50 14.790 14.52 0.8819 5.5450 3.291 2.7040 5.111
## 51 14.860 14.67 0.8676 5.6780 3.258 2.1290 5.351
## 52 14.430 14.40 0.8751 5.5850 3.272 3.9750 5.144
## 53 15.780 14.91 0.8923 5.6740 3.434 5.5930 5.136
## 54 14.490 14.61 0.8538 5.7150 3.113 4.1160 5.396
## 55 14.330 14.28 0.8831 5.5040 3.199 3.3280 5.224
## 56 14.520 14.60 0.8557 5.7410 3.113 1.4810 5.487
## 57 15.030 14.77 0.8658 5.7020 3.212 1.9330 5.439
## 58 14.460 14.35 0.8818 5.3880 3.377 2.8020 5.044
## 59 14.920 14.43 0.9006 5.3840 3.412 1.1420 5.088
## 60 15.380 14.77 0.8857 5.6620 3.419 1.9990 5.222
## 61 12.110 13.47 0.8392 5.1590 3.032 1.5020 4.519
## 62 11.420 12.86 0.8683 5.0080 2.850 2.7000 NA
## 63 1.000 NA NA NA NA NA NA
## 64 11.230 12.63 0.8840 4.9020 2.879 2.2690 4.703
## 65 12.360 13.19 0.8923 5.0760 3.042 3.2200 4.605
## 66 13.220 13.84 0.8680 5.3950 3.070 4.1570 5.088
## 67 12.780 13.57 0.8716 5.2620 3.026 1.1760 4.782
## 68 12.880 13.50 0.8879 5.1390 3.119 2.3520 4.607
## 69 14.340 14.37 0.8726 5.6300 3.190 1.3130 5.150
## 70 14.010 14.29 0.8625 5.6090 3.158 2.2170 5.132
## 71 14.370 14.39 0.8726 5.5690 3.153 1.4640 5.300
## 72 1.000 NA NA NA NA NA NA
## 73 12.730 13.75 0.8458 5.4120 2.882 3.5330 5.067
## 74 17.630 15.98 0.8673 6.1910 3.561 4.0760 6.060
## 75 16.840 15.67 0.8623 5.9980 3.484 4.6750 5.877
## 76 17.260 15.73 0.8763 5.9780 3.594 4.5390 5.791
## 77 19.110 16.26 0.9081 6.1540 3.930 2.9360 6.079
## 78 16.820 15.51 0.8786 6.0170 3.486 4.0040 5.841
## 79 16.770 15.62 0.8638 5.9270 3.438 4.9200 5.795
## 80 17.320 15.91 0.8599 6.0640 3.403 3.8240 5.922
## 81 20.710 17.23 0.8763 6.5790 3.814 4.4510 6.451
## 82 18.940 16.49 0.8750 6.4450 3.639 5.0640 6.362
## 83 17.120 15.55 0.8892 5.8500 3.566 2.8580 5.746
## 84 16.530 15.34 0.8823 5.8750 3.467 5.5320 5.880
## 85 18.720 16.19 0.8977 6.0060 3.857 5.3240 5.879
## 86 20.200 16.89 0.8894 6.2850 3.864 5.1730 6.187
## 87 19.570 16.74 0.8779 6.3840 3.772 1.4720 6.273
## 88 19.510 16.71 0.8780 6.3660 3.801 2.9620 6.185
## 89 18.270 16.09 0.8870 6.1730 3.651 2.4430 6.197
## 90 18.880 16.26 0.8969 6.0840 3.764 1.6490 6.109
## 91 18.980 16.66 0.8590 6.5490 3.670 3.6910 6.498
## 92 21.180 17.21 0.8989 6.5730 4.033 5.7800 6.231
## 93 20.880 17.05 0.9031 6.4500 4.032 5.0160 6.321
## 94 20.100 16.99 0.8746 6.5810 3.785 1.9550 6.449
## 95 18.760 16.20 0.8984 6.1720 3.796 3.1200 6.053
## 96 18.810 16.29 0.8906 6.2720 3.693 3.2370 6.053
## 97 18.590 16.05 0.9066 6.0370 3.860 6.0010 5.877
## 98 18.360 16.52 0.8452 6.6660 3.485 4.9330 6.448
## 99 16.870 15.65 0.8648 6.1390 3.463 3.6960 5.967
## 100 19.310 16.59 0.8815 6.3410 3.810 3.4770 6.238
## 101 18.980 16.57 0.8687 6.4490 3.552 2.1440 6.453
## 102 18.170 16.26 0.8637 6.2710 3.512 2.8530 6.273
## 103 18.720 16.34 0.8810 6.2190 3.684 2.1880 6.097
## 104 16.410 15.25 0.8866 5.7180 3.525 4.2170 5.618
## 105 17.990 15.86 0.8992 5.8900 3.694 2.0680 5.837
## 106 19.460 16.50 0.8985 6.1130 3.892 4.3080 6.009
## 107 19.180 16.63 0.8717 6.3690 3.681 3.3570 6.229
## 108 18.950 16.42 0.8829 6.2480 3.755 3.3680 6.148
## 109 18.830 16.29 0.8917 6.0370 3.786 2.5530 5.879
## 110 18.850 16.17 0.9056 6.1520 3.806 2.8430 6.200
## 111 2.000 NA NA NA NA NA NA
## 112 17.630 15.86 0.8800 6.0330 3.573 3.7470 5.929
## 113 19.940 16.92 0.8752 6.6750 3.763 3.2520 6.550
## 114 18.550 16.22 0.8865 6.1530 3.674 1.7380 5.894
## 115 18.450 16.12 0.8921 6.1070 3.769 2.2350 5.794
## 116 19.380 16.72 0.8716 6.3030 3.791 3.6780 5.965
## 117 19.130 16.31 0.9035 6.1830 3.902 2.1090 5.924
## 118 19.140 16.61 0.8722 6.2590 3.737 6.6820 6.053
## 119 20.970 17.25 0.8859 6.5630 3.991 4.6770 6.316
## 120 19.060 16.45 0.8854 6.4160 3.719 2.2480 6.163
## 121 18.960 16.20 0.9077 6.0510 3.897 4.3340 5.750
## 122 19.150 16.45 0.8890 6.2450 3.815 3.0840 6.185
## 123 18.890 16.23 0.9008 6.2270 3.769 3.6390 5.966
## 124 20.030 16.90 0.8811 6.4930 3.857 3.0630 6.320
## 125 20.240 16.91 0.8897 6.3150 3.962 5.9010 6.188
## 126 18.140 16.12 0.8772 6.0590 3.563 3.6190 6.011
## 127 16.170 15.38 0.8588 5.7620 3.387 4.2860 5.703
## 128 18.430 15.97 0.9077 5.9800 3.771 2.9840 5.905
## 129 15.990 14.89 0.9064 5.3630 3.582 3.3360 5.144
## 130 18.750 16.18 0.8999 6.1110 3.869 4.1880 5.992
## 131 18.650 16.41 0.8698 6.2850 3.594 4.3910 6.102
## 132 17.980 15.85 0.8993 5.9790 3.687 2.2570 5.919
## 133 20.160 17.03 0.8735 6.5130 3.773 1.9100 6.185
## 134 17.550 15.66 0.8991 5.7910 3.690 5.3660 5.661
## 135 18.300 15.89 0.9108 5.9790 3.755 2.8370 5.962
## 136 18.940 16.32 0.8942 6.1440 3.825 2.9080 5.949
## 137 15.380 14.90 0.8706 5.8840 3.268 4.4620 5.795
## 138 16.160 15.33 0.8644 5.8450 3.395 4.2660 5.795
## 139 15.560 14.89 0.8823 5.7760 3.408 4.9720 5.847
## 140 15.380 14.66 0.8990 5.4770 3.465 3.6000 NA
## 141 2.000 NA NA NA NA NA NA
## 142 17.360 15.76 0.8785 6.1450 3.574 3.5260 5.971
## 143 15.570 15.15 0.8527 5.9200 3.231 2.6400 5.879
## 144 15.600 15.11 0.8580 5.8320 3.286 2.7250 5.752
## 145 16.230 15.18 0.8850 5.8720 3.472 3.7690 5.922
## 146 13.070 13.92 0.8480 5.4720 2.994 5.3040 5.395
## 147 13.320 13.94 0.8613 5.5410 3.073 7.0350 5.440
## 148 13.340 13.95 0.8620 5.3890 3.074 5.9950 5.307
## 149 12.220 13.32 0.8652 5.2240 2.967 5.4690 5.221
## 150 11.820 13.40 0.8274 5.3140 2.777 4.4710 5.178
## 151 11.210 13.13 0.8167 5.2790 2.687 6.1690 5.275
## 152 11.430 13.13 0.8335 5.1760 2.719 2.2210 5.132
## 153 12.490 13.46 0.8658 5.2670 2.967 4.4210 5.002
## 154 12.700 13.71 0.8491 5.3860 2.911 3.2600 5.316
## 155 10.790 12.93 0.8107 5.3170 2.648 5.4620 5.194
## 156 11.830 13.23 0.8496 5.2630 2.840 5.1950 5.307
## 157 12.010 13.52 0.8249 5.4050 2.776 6.9920 5.270
## 158 12.260 13.60 0.8333 5.4080 2.833 4.7560 5.360
## 159 11.180 13.04 0.8266 5.2200 2.693 3.3320 5.001
## 160 11.360 13.05 0.8382 5.1750 2.755 4.0480 5.263
## 161 11.190 13.05 0.8253 5.2500 2.675 5.8130 5.219
## 162 11.340 12.87 0.8596 5.0530 2.849 3.3470 5.003
## 163 12.130 13.73 0.8081 5.3940 2.745 4.8250 5.220
## 164 11.750 13.52 0.8082 5.4440 2.678 4.3780 5.310
## 165 11.490 13.22 0.8263 5.3040 2.695 5.3880 5.310
## 166 12.540 13.67 0.8425 5.4510 2.879 3.0820 5.491
## 167 12.020 13.33 0.8503 5.3500 2.810 4.2710 5.308
## 168 12.050 13.41 0.8416 5.2670 2.847 4.9880 5.046
## 169 12.550 13.57 0.8558 5.3330 2.968 4.4190 5.176
## 170 11.140 12.79 0.8558 5.0110 2.794 6.3880 5.049
## 171 12.100 13.15 0.8793 5.1050 2.941 2.2010 5.056
## 172 12.440 13.59 0.8462 5.3190 2.897 4.9240 5.270
## 173 12.150 13.45 0.8443 5.4170 2.837 3.6380 5.338
## 174 11.350 13.12 0.8291 5.1760 2.668 4.3370 5.132
## 175 11.240 13.00 NA 0.8359 5.090 2.7150 3.521
## 176 3.000 NA NA NA NA NA NA
## 177 11.020 13.00 NA 0.8189 5.325 2.7010 6.735
## 178 3.000 NA NA NA NA NA NA
## 179 11.550 13.10 0.8455 5.1670 2.845 6.7150 4.956
## 180 11.270 12.97 0.8419 5.0880 2.763 4.3090 5.000
## 181 3.000 NA NA NA NA NA NA
## 182 11.400 13.08 0.8375 5.1360 2.763 5.5880 5.089
## 183 10.830 12.96 0.8099 5.2780 2.641 5.1820 5.185
## 184 10.800 12.57 0.8590 4.9810 2.821 4.7730 5.063
## 185 11.260 13.01 0.8355 5.1860 2.710 5.3350 5.092
## 186 10.740 12.73 0.8329 5.1450 2.642 4.7020 4.963
## 187 11.480 13.05 0.8473 5.1800 2.758 5.8760 5.002
## 188 12.210 13.47 0.8453 5.3570 2.893 1.6610 5.178
## 189 11.410 12.95 0.8560 5.0900 2.775 4.9570 4.825
## 190 12.460 13.41 0.8706 5.2360 3.017 4.9870 5.147
## 191 12.190 13.36 0.8579 5.2400 2.909 4.8570 5.158
## 192 11.650 13.07 0.8575 5.1080 2.850 5.2090 5.135
## 193 12.890 13.77 0.8541 5.4950 3.026 6.1850 5.316
## 194 11.560 13.31 0.8198 5.3630 2.683 4.0620 5.182
## 195 11.810 13.45 0.8198 5.4130 2.716 4.8980 5.352
## 196 10.910 12.80 0.8372 5.0880 2.675 4.1790 4.956
## 197 11.230 12.82 0.8594 5.0890 2.821 7.5240 4.957
## 198 10.590 12.41 0.8648 4.8990 2.787 4.9750 4.794
## 199 10.930 12.80 0.8390 5.0460 2.717 5.3980 5.045
## 200 11.270 12.86 0.8563 5.0910 2.804 3.9850 5.001
## 201 11.870 13.02 0.8795 5.1320 2.953 3.5970 5.132
## 202 10.820 12.83 0.8256 5.1800 2.630 4.8530 5.089
## 203 12.110 13.27 0.8639 5.2360 2.975 4.1320 5.012
## 204 12.800 13.47 0.8860 5.1600 3.126 4.8730 4.914
## 205 12.790 13.53 0.8786 5.2240 3.054 5.4830 4.958
## 206 13.370 13.78 0.8849 5.3200 3.128 4.6700 5.091
## 207 12.620 13.67 0.8481 5.4100 2.911 3.3060 5.231
## 208 12.760 13.38 0.8964 5.0730 3.155 2.8280 4.830
## 209 12.380 13.44 0.8609 5.2190 2.989 5.4720 5.045
## 210 12.670 13.32 0.8977 4.9840 3.135 2.3000 NA
## 211 3.000 NA NA NA NA NA NA
## 212 11.180 12.72 0.8680 5.0090 2.810 4.0510 4.828
## 213 12.700 13.41 0.8874 5.1830 3.091 8.4560 5.000
## 214 3.000 NA NA NA NA NA NA
## 215 12.370 13.47 0.8567 5.2040 2.960 3.9190 5.001
## 216 12.190 13.20 0.8783 5.1370 2.981 3.6310 4.870
## 217 11.230 12.88 0.8511 5.1400 2.795 4.3250 5.003
## 218 13.200 13.66 0.8883 5.2360 3.232 8.3150 5.056
## 219 11.840 13.21 0.8521 5.1750 2.836 3.5980 5.044
## 220 12.300 13.34 0.8684 5.2430 2.974 5.6370 5.063 feature_name<-c('area', 'perimeter', 'compactness', 'lenght_of_Kernel', 'width.of.kernel','asymentry.coefficient', 'lenght.of.kernel.groove')
colnames(sdf)<- feature_name
str(sdf)## 'data.frame': 220 obs. of 7 variables:
## $ area : num 14.9 14.3 13.8 16.1 14.4 ...
## $ perimeter : num 14.6 14.1 13.9 15 14.2 ...
## $ compactness : num 0.881 0.905 0.895 0.903 0.895 ...
## $ lenght_of_Kernel : num 5.55 5.29 5.32 5.66 5.39 ...
## $ width.of.kernel : num 3.33 3.34 3.38 3.56 3.31 ...
## $ asymentry.coefficient : num 1.02 2.7 2.26 1.35 2.46 ...
## $ lenght.of.kernel.groove: num 4.96 4.83 4.8 5.17 4.96 ... summary(sdf)## area perimeter compactness lenght_of_Kernel
## Min. : 1.00 Min. : 1.00 Min. :0.8081 Min. :0.8189
## 1st Qu.:12.11 1st Qu.:13.43 1st Qu.:0.8576 1st Qu.:5.2430
## Median :14.11 Median :14.28 Median :0.8740 Median :5.5160
## Mean :14.29 Mean :14.43 Mean :0.8713 Mean :5.5630
## 3rd Qu.:17.10 3rd Qu.:15.70 3rd Qu.:0.8878 3rd Qu.:5.9800
## Max. :21.18 Max. :17.25 Max. :0.9183 Max. :6.6750
## NA's :1 NA's :9 NA's :14 NA's :11
## width.of.kernel asymentry.coefficient lenght.of.kernel.groove
## Min. :2.630 Min. :0.7651 Min. :3.485
## 1st Qu.:2.955 1st Qu.:2.6400 1st Qu.:5.045
## Median :3.244 Median :3.6000 Median :5.228
## Mean :3.281 Mean :3.7006 Mean :5.408
## 3rd Qu.:3.568 3rd Qu.:4.7730 3rd Qu.:5.879
## Max. :5.325 Max. :8.4560 Max. :6.735
## NA's :12 NA's :11 NA's :15 is.na(sdf)## area perimeter compactness lenght_of_Kernel width.of.kernel
## [1,] FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE FALSE
## [7,] FALSE FALSE FALSE FALSE FALSE
## [8,] TRUE FALSE TRUE TRUE TRUE
## [9,] FALSE FALSE FALSE FALSE FALSE
## [10,] FALSE FALSE FALSE FALSE FALSE
## [11,] FALSE FALSE FALSE FALSE FALSE
## [12,] FALSE FALSE FALSE FALSE FALSE
## [13,] FALSE FALSE FALSE FALSE FALSE
## [14,] FALSE FALSE FALSE FALSE FALSE
## [15,] FALSE FALSE FALSE FALSE FALSE
## [16,] FALSE FALSE FALSE FALSE FALSE
## [17,] FALSE FALSE FALSE FALSE FALSE
## [18,] FALSE FALSE FALSE FALSE FALSE
## [19,] FALSE FALSE FALSE FALSE FALSE
## [20,] FALSE FALSE FALSE FALSE FALSE
## [21,] FALSE FALSE FALSE FALSE FALSE
## [22,] FALSE FALSE FALSE FALSE FALSE
## [23,] FALSE FALSE FALSE FALSE FALSE
## [24,] FALSE FALSE FALSE FALSE FALSE
## [25,] FALSE FALSE FALSE FALSE FALSE
## [26,] FALSE FALSE FALSE FALSE FALSE
## [27,] FALSE FALSE FALSE FALSE FALSE
## [28,] FALSE FALSE FALSE FALSE FALSE
## [29,] FALSE FALSE FALSE FALSE FALSE
## [30,] FALSE FALSE FALSE FALSE FALSE
## [31,] FALSE FALSE FALSE FALSE FALSE
## [32,] FALSE FALSE FALSE FALSE FALSE
## [33,] FALSE FALSE FALSE FALSE FALSE
## [34,] FALSE FALSE FALSE FALSE FALSE
## [35,] FALSE FALSE FALSE FALSE FALSE
## [36,] FALSE FALSE TRUE FALSE TRUE
## [37,] FALSE FALSE TRUE TRUE TRUE
## [38,] FALSE FALSE FALSE FALSE FALSE
## [39,] FALSE FALSE FALSE FALSE FALSE
## [40,] FALSE FALSE FALSE FALSE FALSE
## [41,] FALSE FALSE FALSE FALSE FALSE
## [42,] FALSE FALSE FALSE FALSE FALSE
## [43,] FALSE FALSE FALSE FALSE FALSE
## [44,] FALSE FALSE FALSE FALSE FALSE
## [45,] FALSE FALSE FALSE FALSE FALSE
## [46,] FALSE FALSE FALSE FALSE FALSE
## [47,] FALSE FALSE FALSE FALSE FALSE
## [48,] FALSE FALSE FALSE FALSE FALSE
## [49,] FALSE FALSE FALSE FALSE FALSE
## [50,] FALSE FALSE FALSE FALSE FALSE
## [51,] FALSE FALSE FALSE FALSE FALSE
## [52,] FALSE FALSE FALSE FALSE FALSE
## [53,] FALSE FALSE FALSE FALSE FALSE
## [54,] FALSE FALSE FALSE FALSE FALSE
## [55,] FALSE FALSE FALSE FALSE FALSE
## [56,] FALSE FALSE FALSE FALSE FALSE
## [57,] FALSE FALSE FALSE FALSE FALSE
## [58,] FALSE FALSE FALSE FALSE FALSE
## [59,] FALSE FALSE FALSE FALSE FALSE
## [60,] FALSE FALSE FALSE FALSE FALSE
## [61,] FALSE FALSE FALSE FALSE FALSE
## [62,] FALSE FALSE FALSE FALSE FALSE
## [63,] FALSE TRUE TRUE TRUE TRUE
## [64,] FALSE FALSE FALSE FALSE FALSE
## [65,] FALSE FALSE FALSE FALSE FALSE
## [66,] FALSE FALSE FALSE FALSE FALSE
## [67,] FALSE FALSE FALSE FALSE FALSE
## [68,] FALSE FALSE FALSE FALSE FALSE
## [69,] FALSE FALSE FALSE FALSE FALSE
## [70,] FALSE FALSE FALSE FALSE FALSE
## [71,] FALSE FALSE FALSE FALSE FALSE
## [72,] FALSE TRUE TRUE TRUE TRUE
## [73,] FALSE FALSE FALSE FALSE FALSE
## [74,] FALSE FALSE FALSE FALSE FALSE
## [75,] FALSE FALSE FALSE FALSE FALSE
## [76,] FALSE FALSE FALSE FALSE FALSE
## [77,] FALSE FALSE FALSE FALSE FALSE
## [78,] FALSE FALSE FALSE FALSE FALSE
## [79,] FALSE FALSE FALSE FALSE FALSE
## [80,] FALSE FALSE FALSE FALSE FALSE
## [81,] FALSE FALSE FALSE FALSE FALSE
## [82,] FALSE FALSE FALSE FALSE FALSE
## [83,] FALSE FALSE FALSE FALSE FALSE
## [84,] FALSE FALSE FALSE FALSE FALSE
## [85,] FALSE FALSE FALSE FALSE FALSE
## [86,] FALSE FALSE FALSE FALSE FALSE
## [87,] FALSE FALSE FALSE FALSE FALSE
## [88,] FALSE FALSE FALSE FALSE FALSE
## [89,] FALSE FALSE FALSE FALSE FALSE
## [90,] FALSE FALSE FALSE FALSE FALSE
## [91,] FALSE FALSE FALSE FALSE FALSE
## [92,] FALSE FALSE FALSE FALSE FALSE
## [93,] FALSE FALSE FALSE FALSE FALSE
## [94,] FALSE FALSE FALSE FALSE FALSE
## [95,] FALSE FALSE FALSE FALSE FALSE
## [96,] FALSE FALSE FALSE FALSE FALSE
## [97,] FALSE FALSE FALSE FALSE FALSE
## [98,] FALSE FALSE FALSE FALSE FALSE
## [99,] FALSE FALSE FALSE FALSE FALSE
## [100,] FALSE FALSE FALSE FALSE FALSE
## [101,] FALSE FALSE FALSE FALSE FALSE
## [102,] FALSE FALSE FALSE FALSE FALSE
## [103,] FALSE FALSE FALSE FALSE FALSE
## [104,] FALSE FALSE FALSE FALSE FALSE
## [105,] FALSE FALSE FALSE FALSE FALSE
## [106,] FALSE FALSE FALSE FALSE FALSE
## [107,] FALSE FALSE FALSE FALSE FALSE
## [108,] FALSE FALSE FALSE FALSE FALSE
## [109,] FALSE FALSE FALSE FALSE FALSE
## [110,] FALSE FALSE FALSE FALSE FALSE
## [111,] FALSE TRUE TRUE TRUE TRUE
## [112,] FALSE FALSE FALSE FALSE FALSE
## [113,] FALSE FALSE FALSE FALSE FALSE
## [114,] FALSE FALSE FALSE FALSE FALSE
## [115,] FALSE FALSE FALSE FALSE FALSE
## [116,] FALSE FALSE FALSE FALSE FALSE
## [117,] FALSE FALSE FALSE FALSE FALSE
## [118,] FALSE FALSE FALSE FALSE FALSE
## [119,] FALSE FALSE FALSE FALSE FALSE
## [120,] FALSE FALSE FALSE FALSE FALSE
## [121,] FALSE FALSE FALSE FALSE FALSE
## [122,] FALSE FALSE FALSE FALSE FALSE
## [123,] FALSE FALSE FALSE FALSE FALSE
## [124,] FALSE FALSE FALSE FALSE FALSE
## [125,] FALSE FALSE FALSE FALSE FALSE
## [126,] FALSE FALSE FALSE FALSE FALSE
## [127,] FALSE FALSE FALSE FALSE FALSE
## [128,] FALSE FALSE FALSE FALSE FALSE
## [129,] FALSE FALSE FALSE FALSE FALSE
## [130,] FALSE FALSE FALSE FALSE FALSE
## [131,] FALSE FALSE FALSE FALSE FALSE
## [132,] FALSE FALSE FALSE FALSE FALSE
## [133,] FALSE FALSE FALSE FALSE FALSE
## [134,] FALSE FALSE FALSE FALSE FALSE
## [135,] FALSE FALSE FALSE FALSE FALSE
## [136,] FALSE FALSE FALSE FALSE FALSE
## [137,] FALSE FALSE FALSE FALSE FALSE
## [138,] FALSE FALSE FALSE FALSE FALSE
## [139,] FALSE FALSE FALSE FALSE FALSE
## [140,] FALSE FALSE FALSE FALSE FALSE
## [141,] FALSE TRUE TRUE TRUE TRUE
## [142,] FALSE FALSE FALSE FALSE FALSE
## [143,] FALSE FALSE FALSE FALSE FALSE
## [144,] FALSE FALSE FALSE FALSE FALSE
## [145,] FALSE FALSE FALSE FALSE FALSE
## [146,] FALSE FALSE FALSE FALSE FALSE
## [147,] FALSE FALSE FALSE FALSE FALSE
## [148,] FALSE FALSE FALSE FALSE FALSE
## [149,] FALSE FALSE FALSE FALSE FALSE
## [150,] FALSE FALSE FALSE FALSE FALSE
## [151,] FALSE FALSE FALSE FALSE FALSE
## [152,] FALSE FALSE FALSE FALSE FALSE
## [153,] FALSE FALSE FALSE FALSE FALSE
## [154,] FALSE FALSE FALSE FALSE FALSE
## [155,] FALSE FALSE FALSE FALSE FALSE
## [156,] FALSE FALSE FALSE FALSE FALSE
## [157,] FALSE FALSE FALSE FALSE FALSE
## [158,] FALSE FALSE FALSE FALSE FALSE
## [159,] FALSE FALSE FALSE FALSE FALSE
## [160,] FALSE FALSE FALSE FALSE FALSE
## [161,] FALSE FALSE FALSE FALSE FALSE
## [162,] FALSE FALSE FALSE FALSE FALSE
## [163,] FALSE FALSE FALSE FALSE FALSE
## [164,] FALSE FALSE FALSE FALSE FALSE
## [165,] FALSE FALSE FALSE FALSE FALSE
## [166,] FALSE FALSE FALSE FALSE FALSE
## [167,] FALSE FALSE FALSE FALSE FALSE
## [168,] FALSE FALSE FALSE FALSE FALSE
## [169,] FALSE FALSE FALSE FALSE FALSE
## [170,] FALSE FALSE FALSE FALSE FALSE
## [171,] FALSE FALSE FALSE FALSE FALSE
## [172,] FALSE FALSE FALSE FALSE FALSE
## [173,] FALSE FALSE FALSE FALSE FALSE
## [174,] FALSE FALSE FALSE FALSE FALSE
## [175,] FALSE FALSE TRUE FALSE FALSE
## [176,] FALSE TRUE TRUE TRUE TRUE
## [177,] FALSE FALSE TRUE FALSE FALSE
## [178,] FALSE TRUE TRUE TRUE TRUE
## [179,] FALSE FALSE FALSE FALSE FALSE
## [180,] FALSE FALSE FALSE FALSE FALSE
## [181,] FALSE TRUE TRUE TRUE TRUE
## [182,] FALSE FALSE FALSE FALSE FALSE
## [183,] FALSE FALSE FALSE FALSE FALSE
## [184,] FALSE FALSE FALSE FALSE FALSE
## [185,] FALSE FALSE FALSE FALSE FALSE
## [186,] FALSE FALSE FALSE FALSE FALSE
## [187,] FALSE FALSE FALSE FALSE FALSE
## [188,] FALSE FALSE FALSE FALSE FALSE
## [189,] FALSE FALSE FALSE FALSE FALSE
## [190,] FALSE FALSE FALSE FALSE FALSE
## [191,] FALSE FALSE FALSE FALSE FALSE
## [192,] FALSE FALSE FALSE FALSE FALSE
## [193,] FALSE FALSE FALSE FALSE FALSE
## [194,] FALSE FALSE FALSE FALSE FALSE
## [195,] FALSE FALSE FALSE FALSE FALSE
## [196,] FALSE FALSE FALSE FALSE FALSE
## [197,] FALSE FALSE FALSE FALSE FALSE
## [198,] FALSE FALSE FALSE FALSE FALSE
## [199,] FALSE FALSE FALSE FALSE FALSE
## [200,] FALSE FALSE FALSE FALSE FALSE
## [201,] FALSE FALSE FALSE FALSE FALSE
## [202,] FALSE FALSE FALSE FALSE FALSE
## [203,] FALSE FALSE FALSE FALSE FALSE
## [204,] FALSE FALSE FALSE FALSE FALSE
## [205,] FALSE FALSE FALSE FALSE FALSE
## [206,] FALSE FALSE FALSE FALSE FALSE
## [207,] FALSE FALSE FALSE FALSE FALSE
## [208,] FALSE FALSE FALSE FALSE FALSE
## [209,] FALSE FALSE FALSE FALSE FALSE
## [210,] FALSE FALSE FALSE FALSE FALSE
## [211,] FALSE TRUE TRUE TRUE TRUE
## [212,] FALSE FALSE FALSE FALSE FALSE
## [213,] FALSE FALSE FALSE FALSE FALSE
## [214,] FALSE TRUE TRUE TRUE TRUE
## [215,] FALSE FALSE FALSE FALSE FALSE
## [216,] FALSE FALSE FALSE FALSE FALSE
## [217,] FALSE FALSE FALSE FALSE FALSE
## [218,] FALSE FALSE FALSE FALSE FALSE
## [219,] FALSE FALSE FALSE FALSE FALSE
## [220,] FALSE FALSE FALSE FALSE FALSE
## asymentry.coefficient lenght.of.kernel.groove
## [1,] FALSE FALSE
## [2,] FALSE FALSE
## [3,] FALSE FALSE
## [4,] FALSE FALSE
## [5,] FALSE FALSE
## [6,] FALSE FALSE
## [7,] FALSE TRUE
## [8,] TRUE TRUE
## [9,] FALSE FALSE
## [10,] FALSE FALSE
## [11,] FALSE FALSE
## [12,] FALSE FALSE
## [13,] FALSE FALSE
## [14,] FALSE FALSE
## [15,] FALSE FALSE
## [16,] FALSE FALSE
## [17,] FALSE FALSE
## [18,] FALSE FALSE
## [19,] FALSE FALSE
## [20,] FALSE FALSE
## [21,] FALSE FALSE
## [22,] FALSE FALSE
## [23,] FALSE FALSE
## [24,] FALSE FALSE
## [25,] FALSE FALSE
## [26,] FALSE FALSE
## [27,] FALSE FALSE
## [28,] FALSE FALSE
## [29,] FALSE FALSE
## [30,] FALSE FALSE
## [31,] FALSE FALSE
## [32,] FALSE FALSE
## [33,] FALSE FALSE
## [34,] FALSE FALSE
## [35,] FALSE FALSE
## [36,] FALSE FALSE
## [37,] TRUE TRUE
## [38,] FALSE FALSE
## [39,] FALSE FALSE
## [40,] FALSE FALSE
## [41,] FALSE FALSE
## [42,] FALSE FALSE
## [43,] FALSE FALSE
## [44,] FALSE FALSE
## [45,] FALSE FALSE
## [46,] FALSE FALSE
## [47,] FALSE FALSE
## [48,] FALSE FALSE
## [49,] FALSE FALSE
## [50,] FALSE FALSE
## [51,] FALSE FALSE
## [52,] FALSE FALSE
## [53,] FALSE FALSE
## [54,] FALSE FALSE
## [55,] FALSE FALSE
## [56,] FALSE FALSE
## [57,] FALSE FALSE
## [58,] FALSE FALSE
## [59,] FALSE FALSE
## [60,] FALSE FALSE
## [61,] FALSE FALSE
## [62,] FALSE TRUE
## [63,] TRUE TRUE
## [64,] FALSE FALSE
## [65,] FALSE FALSE
## [66,] FALSE FALSE
## [67,] FALSE FALSE
## [68,] FALSE FALSE
## [69,] FALSE FALSE
## [70,] FALSE FALSE
## [71,] FALSE FALSE
## [72,] TRUE TRUE
## [73,] FALSE FALSE
## [74,] FALSE FALSE
## [75,] FALSE FALSE
## [76,] FALSE FALSE
## [77,] FALSE FALSE
## [78,] FALSE FALSE
## [79,] FALSE FALSE
## [80,] FALSE FALSE
## [81,] FALSE FALSE
## [82,] FALSE FALSE
## [83,] FALSE FALSE
## [84,] FALSE FALSE
## [85,] FALSE FALSE
## [86,] FALSE FALSE
## [87,] FALSE FALSE
## [88,] FALSE FALSE
## [89,] FALSE FALSE
## [90,] FALSE FALSE
## [91,] FALSE FALSE
## [92,] FALSE FALSE
## [93,] FALSE FALSE
## [94,] FALSE FALSE
## [95,] FALSE FALSE
## [96,] FALSE FALSE
## [97,] FALSE FALSE
## [98,] FALSE FALSE
## [99,] FALSE FALSE
## [100,] FALSE FALSE
## [101,] FALSE FALSE
## [102,] FALSE FALSE
## [103,] FALSE FALSE
## [104,] FALSE FALSE
## [105,] FALSE FALSE
## [106,] FALSE FALSE
## [107,] FALSE FALSE
## [108,] FALSE FALSE
## [109,] FALSE FALSE
## [110,] FALSE FALSE
## [111,] TRUE TRUE
## [112,] FALSE FALSE
## [113,] FALSE FALSE
## [114,] FALSE FALSE
## [115,] FALSE FALSE
## [116,] FALSE FALSE
## [117,] FALSE FALSE
## [118,] FALSE FALSE
## [119,] FALSE FALSE
## [120,] FALSE FALSE
## [121,] FALSE FALSE
## [122,] FALSE FALSE
## [123,] FALSE FALSE
## [124,] FALSE FALSE
## [125,] FALSE FALSE
## [126,] FALSE FALSE
## [127,] FALSE FALSE
## [128,] FALSE FALSE
## [129,] FALSE FALSE
## [130,] FALSE FALSE
## [131,] FALSE FALSE
## [132,] FALSE FALSE
## [133,] FALSE FALSE
## [134,] FALSE FALSE
## [135,] FALSE FALSE
## [136,] FALSE FALSE
## [137,] FALSE FALSE
## [138,] FALSE FALSE
## [139,] FALSE FALSE
## [140,] FALSE TRUE
## [141,] TRUE TRUE
## [142,] FALSE FALSE
## [143,] FALSE FALSE
## [144,] FALSE FALSE
## [145,] FALSE FALSE
## [146,] FALSE FALSE
## [147,] FALSE FALSE
## [148,] FALSE FALSE
## [149,] FALSE FALSE
## [150,] FALSE FALSE
## [151,] FALSE FALSE
## [152,] FALSE FALSE
## [153,] FALSE FALSE
## [154,] FALSE FALSE
## [155,] FALSE FALSE
## [156,] FALSE FALSE
## [157,] FALSE FALSE
## [158,] FALSE FALSE
## [159,] FALSE FALSE
## [160,] FALSE FALSE
## [161,] FALSE FALSE
## [162,] FALSE FALSE
## [163,] FALSE FALSE
## [164,] FALSE FALSE
## [165,] FALSE FALSE
## [166,] FALSE FALSE
## [167,] FALSE FALSE
## [168,] FALSE FALSE
## [169,] FALSE FALSE
## [170,] FALSE FALSE
## [171,] FALSE FALSE
## [172,] FALSE FALSE
## [173,] FALSE FALSE
## [174,] FALSE FALSE
## [175,] FALSE FALSE
## [176,] TRUE TRUE
## [177,] FALSE FALSE
## [178,] TRUE TRUE
## [179,] FALSE FALSE
## [180,] FALSE FALSE
## [181,] TRUE TRUE
## [182,] FALSE FALSE
## [183,] FALSE FALSE
## [184,] FALSE FALSE
## [185,] FALSE FALSE
## [186,] FALSE FALSE
## [187,] FALSE FALSE
## [188,] FALSE FALSE
## [189,] FALSE FALSE
## [190,] FALSE FALSE
## [191,] FALSE FALSE
## [192,] FALSE FALSE
## [193,] FALSE FALSE
## [194,] FALSE FALSE
## [195,] FALSE FALSE
## [196,] FALSE FALSE
## [197,] FALSE FALSE
## [198,] FALSE FALSE
## [199,] FALSE FALSE
## [200,] FALSE FALSE
## [201,] FALSE FALSE
## [202,] FALSE FALSE
## [203,] FALSE FALSE
## [204,] FALSE FALSE
## [205,] FALSE FALSE
## [206,] FALSE FALSE
## [207,] FALSE FALSE
## [208,] FALSE FALSE
## [209,] FALSE FALSE
## [210,] FALSE TRUE
## [211,] TRUE TRUE
## [212,] FALSE FALSE
## [213,] FALSE FALSE
## [214,] TRUE TRUE
## [215,] FALSE FALSE
## [216,] FALSE FALSE
## [217,] FALSE FALSE
## [218,] FALSE FALSE
## [219,] FALSE FALSE
## [220,] FALSE FALSE sdf<-na.omit(sdf)
sdf_scale<-scale(sdf)
summary(sdf_scale)## area perimeter compactness lenght_of_Kernel
## Min. :-1.4741 Min. :-1.6584 Min. :-2.6858 Min. :-1.6660
## 1st Qu.:-0.8848 1st Qu.:-0.8568 1st Qu.:-0.5997 1st Qu.:-0.8450
## Median :-0.1810 Median :-0.1732 Median : 0.1077 Median :-0.2220
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.8875 3rd Qu.: 0.9444 3rd Qu.: 0.6902 3rd Qu.: 0.8195
## Max. : 2.1439 Max. : 2.0278 Max. : 2.0249 Max. : 2.3287
## width.of.kernel asymentry.coefficient lenght.of.kernel.groove
## Min. :-1.67149 Min. :-1.96257 Min. :-1.8263
## 1st Qu.:-0.81804 1st Qu.:-0.74280 1st Qu.:-0.7605
## Median :-0.07136 Median :-0.05537 Median :-0.3873
## Mean : 0.00000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.: 0.79395 3rd Qu.: 0.72728 3rd Qu.: 0.9281
## Max. : 2.02700 Max. : 3.14933 Max. : 2.2870 d<-dist(sdf_scale)
h<-hclust(d)
plot(h)
plot(h, hang = 0.1, labels = seeds_dataset[["v8"]],cex = 0.5)
c<-cutree(h,3)
c## 1 2 3 4 5 6 9 10 11 12 13 14 15 16 17 18 19 20 21 22
## 1 1 1 1 1 1 1 1 2 2 1 2 2 1 2 1 1 2 2 2
## 23 24 25 26 27 28 29 30 31 32 33 34 35 38 39 40 41 42 43 44
## 1 2 2 1 2 2 1 2 2 1 2 2 2 1 1 1 2 2 2 2
## 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 64 65 66
## 1 1 2 1 1 1 2 1 2 2 1 2 2 1 1 1 2 2 2 2
## 67 68 69 70 71 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87
## 2 2 2 2 2 2 3 1 1 3 1 1 1 3 3 1 1 3 3 3
## 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
## 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 1 3 3 3
## 108 109 110 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
## 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 3
## 129 130 131 132 133 134 135 136 137 138 139 142 143 144 145 146 147 148 149 150
## 1 3 3 3 3 3 3 3 1 1 1 1 1 1 1 2 2 2 2 2
## 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 171 172 173 174 179 180 182 183 184 185 186 187 188 189 190 191 192 193 194 195
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 196 197 198 199 200 201 202 203 204 205 206 207 208 209 212 213 215 216 217 218
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 219 220
## 2 2 #R Markdown
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00Including Plots
You can also embed plots, for example:
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.