library(readr)
## Warning: package 'readr' was built under R version 4.3.2
seeds_dataset <- read_delim("C:/Users/derek/OneDrive/Desktop/seeds_dataset.txt",
delim = "\t")
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
## Rows: 209 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (1): 1
## dbl (7): 15.26, 14.84, 0.871, 5.763, 3.312, 2.221, 5.22
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
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 -0.4968500
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 11.362705
dist(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 3.643788
d<-dist(scale(iris[,-5]))
h<-hclust(d)
plot(h)
plot(h, hang=-0.1, labels=iris[["species"]], cex=0.5)
clus3 <- cutree(h,3)
clus3
## [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(clus3, iris$Species))
##
## clus3 setosa versicolor virginica
## 1 49 0 0
## 2 1 21 2
## 3 0 29 48
100*(1-sum(diag(cm))/sum(cm))
## [1] 21.33333
library(cluster)
silhouette(clus3, 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 = clus3, dist = d)
## attr(,"class")
## [1] "silhouette"
plot(silhouette(clus3,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])
clus3<-cutree(h,k)
s<-silhouette(clus3,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 0.3248248
sdf<-seeds_dataset[,-8]
View(sdf)
sdf<-seeds_dataset
feature_name <-c('area','perimeter','compactness','length_of_kernel','width.of.kernel','asymetry.coefficient','length.of.kernel.groove')
colnames(sdf)<-feature_name
str(sdf)
## spc_tbl_ [209 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ area : num [1:209] 14.9 14.3 13.8 16.1 14.4 ...
## $ perimeter : num [1:209] 14.6 14.1 13.9 15 14.2 ...
## $ compactness : num [1:209] 0.881 0.905 0.895 0.903 0.895 ...
## $ length_of_kernel : num [1:209] 5.55 5.29 5.32 5.66 5.39 ...
## $ width.of.kernel : num [1:209] 3.33 3.34 3.38 3.56 3.31 ...
## $ asymetry.coefficient : num [1:209] 1.02 2.7 2.26 1.35 2.46 ...
## $ length.of.kernel.groove: num [1:209] 4.96 4.83 4.8 5.17 4.96 ...
## $ NA : chr [1:209] "1" "1" "1" "1" ...
## - attr(*, "spec")=
## .. cols(
## .. `15.26` = col_double(),
## .. `14.84` = col_double(),
## .. `0.871` = col_double(),
## .. `5.763` = col_double(),
## .. `3.312` = col_double(),
## .. `2.221` = col_double(),
## .. `5.22` = col_double(),
## .. `1` = col_character()
## .. )
## - attr(*, "problems")=<externalptr>
is.na(sdf)
## area perimeter compactness length_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,] FALSE FALSE FALSE FALSE FALSE
## [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 TRUE FALSE TRUE
## [36,] FALSE FALSE FALSE FALSE FALSE
## [37,] FALSE FALSE FALSE FALSE FALSE
## [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 FALSE FALSE FALSE FALSE
## [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 FALSE FALSE FALSE FALSE
## [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 FALSE FALSE FALSE FALSE
## [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 FALSE FALSE FALSE FALSE
## [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 TRUE FALSE FALSE
## [170,] FALSE FALSE TRUE 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 FALSE FALSE FALSE
## [176,] FALSE FALSE FALSE FALSE FALSE
## [177,] FALSE FALSE FALSE FALSE FALSE
## [178,] FALSE FALSE FALSE FALSE FALSE
## [179,] FALSE FALSE FALSE FALSE FALSE
## [180,] FALSE FALSE FALSE FALSE FALSE
## [181,] FALSE FALSE FALSE FALSE FALSE
## [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
## asymetry.coefficient length.of.kernel.groove NA
## [1,] FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE
## [7,] FALSE TRUE FALSE
## [8,] FALSE FALSE FALSE
## [9,] FALSE FALSE FALSE
## [10,] FALSE FALSE FALSE
## [11,] FALSE FALSE FALSE
## [12,] FALSE FALSE FALSE
## [13,] FALSE FALSE FALSE
## [14,] FALSE FALSE FALSE
## [15,] FALSE FALSE FALSE
## [16,] FALSE FALSE FALSE
## [17,] FALSE FALSE FALSE
## [18,] FALSE FALSE FALSE
## [19,] FALSE FALSE FALSE
## [20,] FALSE FALSE FALSE
## [21,] FALSE FALSE FALSE
## [22,] FALSE FALSE FALSE
## [23,] FALSE FALSE FALSE
## [24,] FALSE FALSE FALSE
## [25,] FALSE FALSE FALSE
## [26,] FALSE FALSE FALSE
## [27,] FALSE FALSE FALSE
## [28,] FALSE FALSE FALSE
## [29,] FALSE FALSE FALSE
## [30,] FALSE FALSE FALSE
## [31,] FALSE FALSE FALSE
## [32,] FALSE FALSE FALSE
## [33,] FALSE FALSE FALSE
## [34,] FALSE FALSE FALSE
## [35,] FALSE FALSE FALSE
## [36,] FALSE FALSE FALSE
## [37,] FALSE FALSE FALSE
## [38,] FALSE FALSE FALSE
## [39,] FALSE FALSE FALSE
## [40,] FALSE FALSE FALSE
## [41,] FALSE FALSE FALSE
## [42,] FALSE FALSE FALSE
## [43,] FALSE FALSE FALSE
## [44,] FALSE FALSE FALSE
## [45,] FALSE FALSE FALSE
## [46,] FALSE FALSE FALSE
## [47,] FALSE FALSE FALSE
## [48,] FALSE FALSE FALSE
## [49,] FALSE FALSE FALSE
## [50,] FALSE FALSE FALSE
## [51,] FALSE FALSE FALSE
## [52,] FALSE FALSE FALSE
## [53,] FALSE FALSE FALSE
## [54,] FALSE FALSE FALSE
## [55,] FALSE FALSE FALSE
## [56,] FALSE FALSE FALSE
## [57,] FALSE FALSE FALSE
## [58,] FALSE FALSE FALSE
## [59,] FALSE FALSE FALSE
## [60,] FALSE TRUE FALSE
## [61,] FALSE FALSE FALSE
## [62,] FALSE FALSE FALSE
## [63,] FALSE FALSE FALSE
## [64,] FALSE FALSE FALSE
## [65,] FALSE FALSE FALSE
## [66,] FALSE FALSE FALSE
## [67,] FALSE FALSE FALSE
## [68,] FALSE FALSE FALSE
## [69,] FALSE FALSE FALSE
## [70,] FALSE FALSE FALSE
## [71,] FALSE FALSE FALSE
## [72,] FALSE FALSE FALSE
## [73,] FALSE FALSE FALSE
## [74,] FALSE FALSE FALSE
## [75,] FALSE FALSE FALSE
## [76,] FALSE FALSE FALSE
## [77,] FALSE FALSE FALSE
## [78,] FALSE FALSE FALSE
## [79,] FALSE FALSE FALSE
## [80,] FALSE FALSE FALSE
## [81,] FALSE FALSE FALSE
## [82,] FALSE FALSE FALSE
## [83,] FALSE FALSE FALSE
## [84,] FALSE FALSE FALSE
## [85,] FALSE FALSE FALSE
## [86,] FALSE FALSE FALSE
## [87,] FALSE FALSE FALSE
## [88,] FALSE FALSE FALSE
## [89,] FALSE FALSE FALSE
## [90,] FALSE FALSE FALSE
## [91,] FALSE FALSE FALSE
## [92,] FALSE FALSE FALSE
## [93,] FALSE FALSE FALSE
## [94,] FALSE FALSE FALSE
## [95,] FALSE FALSE FALSE
## [96,] FALSE FALSE FALSE
## [97,] FALSE FALSE FALSE
## [98,] FALSE FALSE FALSE
## [99,] FALSE FALSE FALSE
## [100,] FALSE FALSE FALSE
## [101,] FALSE FALSE FALSE
## [102,] FALSE FALSE FALSE
## [103,] FALSE FALSE FALSE
## [104,] FALSE FALSE FALSE
## [105,] FALSE FALSE FALSE
## [106,] FALSE FALSE FALSE
## [107,] FALSE FALSE FALSE
## [108,] FALSE FALSE FALSE
## [109,] FALSE FALSE FALSE
## [110,] FALSE FALSE FALSE
## [111,] FALSE FALSE FALSE
## [112,] FALSE FALSE FALSE
## [113,] FALSE FALSE FALSE
## [114,] FALSE FALSE FALSE
## [115,] FALSE FALSE FALSE
## [116,] FALSE FALSE FALSE
## [117,] FALSE FALSE FALSE
## [118,] FALSE FALSE FALSE
## [119,] FALSE FALSE FALSE
## [120,] FALSE FALSE FALSE
## [121,] FALSE FALSE FALSE
## [122,] FALSE FALSE FALSE
## [123,] FALSE FALSE FALSE
## [124,] FALSE FALSE FALSE
## [125,] FALSE FALSE FALSE
## [126,] FALSE FALSE FALSE
## [127,] FALSE FALSE FALSE
## [128,] FALSE FALSE FALSE
## [129,] FALSE FALSE FALSE
## [130,] FALSE FALSE FALSE
## [131,] FALSE FALSE FALSE
## [132,] FALSE FALSE FALSE
## [133,] FALSE FALSE FALSE
## [134,] FALSE FALSE FALSE
## [135,] FALSE TRUE FALSE
## [136,] FALSE FALSE FALSE
## [137,] FALSE FALSE FALSE
## [138,] FALSE FALSE FALSE
## [139,] FALSE FALSE FALSE
## [140,] FALSE FALSE FALSE
## [141,] FALSE FALSE FALSE
## [142,] FALSE FALSE FALSE
## [143,] FALSE FALSE FALSE
## [144,] FALSE FALSE FALSE
## [145,] FALSE FALSE FALSE
## [146,] FALSE FALSE FALSE
## [147,] FALSE FALSE FALSE
## [148,] FALSE FALSE FALSE
## [149,] FALSE FALSE FALSE
## [150,] FALSE FALSE FALSE
## [151,] FALSE FALSE FALSE
## [152,] FALSE FALSE FALSE
## [153,] FALSE FALSE FALSE
## [154,] FALSE FALSE FALSE
## [155,] FALSE FALSE FALSE
## [156,] FALSE FALSE FALSE
## [157,] FALSE FALSE FALSE
## [158,] FALSE FALSE FALSE
## [159,] FALSE FALSE FALSE
## [160,] FALSE FALSE FALSE
## [161,] FALSE FALSE FALSE
## [162,] FALSE FALSE FALSE
## [163,] FALSE FALSE FALSE
## [164,] FALSE FALSE FALSE
## [165,] FALSE FALSE FALSE
## [166,] FALSE FALSE FALSE
## [167,] FALSE FALSE FALSE
## [168,] FALSE FALSE FALSE
## [169,] FALSE FALSE FALSE
## [170,] FALSE FALSE FALSE
## [171,] FALSE FALSE FALSE
## [172,] FALSE FALSE FALSE
## [173,] FALSE FALSE FALSE
## [174,] FALSE FALSE FALSE
## [175,] FALSE FALSE FALSE
## [176,] FALSE FALSE FALSE
## [177,] FALSE FALSE FALSE
## [178,] FALSE FALSE FALSE
## [179,] FALSE FALSE FALSE
## [180,] FALSE FALSE FALSE
## [181,] FALSE FALSE FALSE
## [182,] FALSE FALSE FALSE
## [183,] FALSE FALSE FALSE
## [184,] FALSE FALSE FALSE
## [185,] FALSE FALSE FALSE
## [186,] FALSE FALSE FALSE
## [187,] FALSE FALSE FALSE
## [188,] FALSE FALSE FALSE
## [189,] FALSE FALSE FALSE
## [190,] FALSE FALSE FALSE
## [191,] FALSE FALSE FALSE
## [192,] FALSE FALSE FALSE
## [193,] FALSE FALSE FALSE
## [194,] FALSE FALSE FALSE
## [195,] FALSE FALSE FALSE
## [196,] FALSE FALSE FALSE
## [197,] FALSE FALSE FALSE
## [198,] FALSE FALSE FALSE
## [199,] FALSE FALSE FALSE
## [200,] FALSE FALSE FALSE
## [201,] FALSE TRUE FALSE
## [202,] FALSE FALSE FALSE
## [203,] FALSE FALSE FALSE
## [204,] FALSE FALSE FALSE
## [205,] FALSE FALSE FALSE
## [206,] FALSE FALSE FALSE
## [207,] FALSE FALSE FALSE
## [208,] FALSE FALSE FALSE
## [209,] FALSE FALSE FALSE
sdf<-na.omit(sdf)
summary(sdf)
## area perimeter compactness length_of_kernel
## Min. :10.59 Min. :12.41 Min. :0.8081 Min. :4.899
## 1st Qu.:12.31 1st Qu.:13.46 1st Qu.:0.8569 1st Qu.:5.264
## Median :14.38 Median :14.36 Median :0.8734 Median :5.541
## Mean :14.90 Mean :14.59 Mean :0.8709 Mean :5.640
## 3rd Qu.:17.50 3rd Qu.:15.83 3rd Qu.:0.8871 3rd Qu.:6.004
## Max. :21.18 Max. :17.25 Max. :0.9183 Max. :6.675
## width.of.kernel asymetry.coefficient length.of.kernel.groove
## Min. :2.630 Min. :0.7651 Min. :4.519
## 1st Qu.:2.954 1st Qu.:2.6002 1st Qu.:5.045
## Median :3.237 Median :3.6345 Median :5.229
## Mean :3.264 Mean :3.7178 Mean :5.421
## 3rd Qu.:3.565 3rd Qu.:4.8120 3rd Qu.:5.879
## Max. :4.033 Max. :8.4560 Max. :6.550
## NA
## Length:202
## Class :character
## Mode :character
##
##
##
#sdf_scale<-scale(sdf)
sdf<-na.omit(sdf)
#summary(sdf_scale)
#d<-dist(sdf_scale)
h<-hclust(d)
plot(h)
plot(h,hang=-0.1, labels=seeds_dataset[["v8"]], ceix=0.5)
## Warning in graphics:::plotHclust(n1, merge, height, order(x$order), hang, :
## "ceix" is not a graphical parameter
## Warning in graphics:::plotHclust(n1, merge, height, order(x$order), hang, :
## "ceix" is not a graphical parameter
## Warning in axis(2, at = pretty(range(height)), ...): "ceix" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...): "ceix"
## is not a graphical parameter
c<-cutree(h,3)
#cm<- table(c, seeds_dataset$type)
dim(c)
## NULL
length(c)
## [1] 150
seeds_na<-na.omit(seeds_dataset)
dim(seeds_na)
## [1] 202 8