![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhvcVkO1lL-ER385JWjTcvurHeV5jPcHigpjbrmqLJE3f04MWozGNUwp9nJx_9top5DOHJ_BUS1N9CLb-iJ_GlF0MVc_5Z1lr6_SMh-DrWCozE6OomzRayR5NK-KkvR1NJ88UwMrGv5rsudQ4zctzjxoLULLJ7oT0N5qBpcCO7qjpQsCyhgRPsksXWSrQ/w640-h480/b1.png) |
K-Means clustering dengan R
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Halo teman-teman, jumpa lagi dengan blog sederhana ini. Kemarin kita sejenak break dengan membahas one way anova, kali ini kita akan melanjutkan belajar bersama tentang salah satu algoritma machine learning dengan jenis unsupervised learning, yaitu algoritma klastering (hierarchy clustering dan kmeans clustering) karena beberapa bahasan sebelumnya kita telah membahas supervised learning. Perbedaan supervised learning dan unsupervised learning sendiri dapat teman-teman baca pada unggahan berikut.
Klastering (Clustering) merupakan salah satu algoritma yang banyak digunakan dalam unsupervised learning. Sebelum jauh pembahasannya, ada baiknya kita bahas kaitan antara K means clustering dan hierarchy clustering. Mengapa kita ulas kaitan kedua istilah ini? Karena dalam pengertiannya, kerapkali membingungkan. Lebih-lebih kalau sudah kita campuradukkan dengan konsep clustering yang menggunakan algoritma supervised learning.
Jadi begini, di alam clustering, kita akan mengenal 2 bentuk pengklasteran, yaitu hierarchy clustering dan non-hierarchy clustering. Hierarchy clustering itu intinya adalah mengelompokan data itu tidak secara bertahap dan di dalam prosesnya jumlah cluster tidak ditentukan terlebih dahulu. Berbeda dengan non-hierarchy clustering yang dalam prosesnya diawali dengan menentukan jumlah cluster terlebih dahulu dan biasanya digunakan untuk data-data yang jumlahnya besar. Posisi K-Means clustering itu merupakan salah satu algoritma pengklasteran non-hierarchy clustering. Sampai di sini semoga telah jelas perbedaannya.
Aspek lain yang membedakan hierarchy clustering dan non-hierarchy clustering adalah ada tidaknya proses operasi dan strukturisasi cluster. Dalam hierarchy clustering, kita akan mengenai 2 istilah, yaitu aglomeratif (agglomerative) dan divisif (divisive). Bedanya, kalau aglomaratif setiap amatan dipandang sebagai 1 cluster kemudian digabung menurut kemiripan (distance similarity) dengan amatan lain yang dipandang cluster sendiri, dan seterusnya hingga berhenti dan diperoleh ukuran cluster yang paling optimal. Sedangkan pada divisif, kita pandang seluruh amatan adalah satu cluster kemudian kita pecah (split) untuk mendapatkan cluster yang homogen (varians dalam cluster minimal dan antar cluster maksimal) hingga kita peroleh sejumlah cluster yang optimal. Sedangkan di dalam non-hierarcy clustering algoritma operasi dan strukturisasi tersebut diabaikan.
Berikutnya, mengapa clustering satu ini masuk ke dalam unsupervised learning? Jelas, sebagaimana ulasan sebelumnya, intinya jika data kita bukan data yang terlabelisasi atau telah ditentukan mana variabel dependen dan independennya, maka algoritma dalam pengelompokannya masuk dalam unsupervised learning.
Pada algoritma K-Means clustering sendiri, bagaimana prinsip dasar kerjanya? Setidak ada kurang lebih 3 prinsip dasarnya. Pertama, karena K-Means clustering ini masuk non-hierarchy clustering, maka kita tentukan dulu jumlah klasternya (K). Kedua, menentukan mana centroid atau titik rata-rata sementara sejumlah K cluster. Ketiga, melakukan perulangan (iterasi) penghitungan kemiripan (similarity) setiap amatan terhadap centroid, biasanya menggunakan jarak (bisa jarak Euclidean atau jarak lainnya), sampai tidak ada satupun amatan yang tidak memiliki kelompok (cluster).
Tujuan utama dari algoritma K-Means clustering ini adalah meminimalkan fungsi obyektif pengelompokan, yaitu dengan memaksimalkan varians antar cluster dan meminimalkan varians di dalam cluster.
Kelemahan dari algoritma K-Means clustering ini ada pada metodenya yang menggunakan rata-rata atau mean. Karena ukuran rata-rata ini merupakan ukuran yang tidak bersifat robust, maka algoritma K-Means clustering ini rentan dipengaruhi oleh adanya pencilan atau outlier.
Baik itu sekilas teori mengenai K-Means clustering. Berikutnya kita akan coba mempraktikkan pengklasteran menggunakan metode K-Means clustering dengan menggunakan R. Data yang kita angkat kali ini merupakan data yang saya peroleh menggunakan teknik web scraping memanfaatkan package rvest mengenai daftar Digital Monster (Digimon) menurut jenis, kekuatan, dan karakteristik lainnya. Data saya peroleh dari situs grindosaur dan bisa teman-teman unduh pada tautan berikut.
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjbRp7rffcCELgLBYeuK5-uFJmkBoUhJ9f4-djmX3vNvLsYYa_6sz7nZi1e9ZMlr7EppTzhE3WS6ZcYzsmtCDS2hW-LaqphB7xRdFa8jd_vDN-DCze07vvcGgp7s5TfdxjCrYJE4jTrr0v8AXqpAziAaJKuZ9Q806hhX9dsDlbj_m-Pqv5MdSJtYDpqHQ/w640-h366/grindosaur.png) |
Tampilan situs grindosaur
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Setelah datanya diunduh, kita dapat mempraktikkan algoritma K-Means clustering menggunakan beberapa code berikut:
#Import Dataset
library(readxl)
digimon <- read_excel("E:/R/Machine Learning/digimon.xlsx")
digimon
## # A tibble: 505 x 6
## Move spcost tipe power atribut inheritable
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Acceleration Boost 6 1 0 10 1
## 2 Adhesive Bubble Blow 2 2 25 20 0
## 3 Agility Charge 6 1 0 10 1
## 4 Aguichant Lèvres 40 3 0 30 0
## 5 Air Bubbles 2 3 30 40 0
## 6 Air Shot 5 3 50 50 0
## 7 Ambush Crunch 10 2 90 10 0
## 8 Amethyst Mandala 20 3 70 30 0
## 9 Animal Nail 15 2 115 60 0
## 10 Anti-Bug 4 1 0 10 1
## # ... with 495 more rows
#Menggunakan Variabel Numeriknya saja
dataku <- digimon[,c(2:6)]
dataku
## # A tibble: 505 x 5
## spcost tipe power atribut inheritable
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 6 1 0 10 1
## 2 2 2 25 20 0
## 3 6 1 0 10 1
## 4 40 3 0 30 0
## 5 2 3 30 40 0
## 6 5 3 50 50 0
## 7 10 2 90 10 0
## 8 20 3 70 30 0
## 9 15 2 115 60 0
## 10 4 1 0 10 1
## # ... with 495 more rows
#Melihat Struktur Data
str(dataku)
## tibble [505 x 5] (S3: tbl_df/tbl/data.frame)
## $ spcost : num [1:505] 6 2 6 40 2 5 10 20 15 4 ...
## $ tipe : num [1:505] 1 2 1 3 3 3 2 3 2 1 ...
## $ power : num [1:505] 0 25 0 0 30 50 90 70 115 0 ...
## $ atribut : num [1:505] 10 20 10 30 40 50 10 30 60 10 ...
## $ inheritable: num [1:505] 1 0 1 0 0 0 0 0 0 1 ...
#Mengattach Data
attach(dataku)
## The following objects are masked from dataku (pos = 23):
##
## atribut, inheritable, power, spcost, tipe
#Ringkasan Data
summary(dataku)
## spcost tipe power atribut inheritable
## Min. : 0.00 Min. :1.000 Min. : 0.00 Min. :10.00 Min. :0.0000
## 1st Qu.: 6.00 1st Qu.:2.000 1st Qu.: 20.00 1st Qu.:20.00 1st Qu.:0.0000
## Median :10.00 Median :2.000 Median : 65.00 Median :40.00 Median :0.0000
## Mean :14.97 Mean :2.212 Mean : 60.59 Mean :45.84 Mean :0.2733
## 3rd Qu.:20.00 3rd Qu.:3.000 3rd Qu.: 95.00 3rd Qu.:70.00 3rd Qu.:1.0000
## Max. :99.00 Max. :3.000 Max. :250.00 Max. :90.00 Max. :1.0000
#Ringkasan Data dengan fungsi Kable
library(kableExtra)
summary(dataku) %>% kable() %>% kable_styling()
|
spcost |
tipe |
power |
atribut |
inheritable |
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Min. : 0.00 |
Min. :1.000 |
Min. : 0.00 |
Min. :10.00 |
Min. :0.0000 |
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1st Qu.: 6.00 |
1st Qu.:2.000 |
1st Qu.: 20.00 |
1st Qu.:20.00 |
1st Qu.:0.0000 |
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Median :10.00 |
Median :2.000 |
Median : 65.00 |
Median :40.00 |
Median :0.0000 |
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Mean :14.97 |
Mean :2.212 |
Mean : 60.59 |
Mean :45.84 |
Mean :0.2733 |
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3rd Qu.:20.00 |
3rd Qu.:3.000 |
3rd Qu.: 95.00 |
3rd Qu.:70.00 |
3rd Qu.:1.0000 |
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Max. :99.00 |
Max. :3.000 |
Max. :250.00 |
Max. :90.00 |
Max. :1.0000 |
#Melihat Sebaran Data dengan Faset Histogram
library(tidyverse)
dataku %>%
gather(power, value, 1:5) %>%
ggplot(aes(x=value)) +
geom_histogram(fill = "steelblue", color = "black", bins = 30) +
facet_wrap(~power, scales = "free_x") +
labs(x = "Nilai", y = "Frekuensi")
![plot of chunk 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) 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Visualisasi 1
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#Matrks Korelasi Antar Variabel
library(reshape2)
corrku <- cor(dataku)
melt_cor <- melt(corrku)
ggplot(data = melt_cor, aes(x = Var1, y = Var2, fill = value)) +
geom_tile(aes(fill = value), colour = "white") +
scale_fill_gradient(low = "white", high = "#ff8c00") +
geom_text(aes(Var1, Var2, label = round(value, 2)), size = 5)
![plot of chunk 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E1F+/uOuZS0nU2v3jsdbbH8K6FX9AJyvosecWvr6GfcZi+X+vC73Lyk4nb/6O/sd9p5NwbKmvJLmLujD3sOyy+/3+/esn6LncCLfR2OS8QQwGit20VXl5CzKXxsJrahrmKR/9chZlP98LLR8YYHKgye1jRdb+14NZi7OBE9mX2r51eAnZax0ZnIW5WNfhJbdU2Bc59/QfnKp9Y9Hg5vn9zRtnrJs8ycunjG+HjP0h3IX5a9GQqsbCpy1d3dp33+79S9jwW35EyxMYe6C3mqrO7XV6XQ6Q7J+JBYXqTXLqa3ecH7P+pFup9PpPCBtLab0bt0jibNbdK8KWGRrXfF20/OJYsKup962/cg2P5ZKzctbl3/QlLwE/opr+wcF3lCNvWffPubrS7/vmpIzhQ9e7lq+eae93BVPT21j67JgU/IS+EGXJ2jb0Jg7rtF/s3vGfH/c4c6Z6a/9XvuV3idTl1QGXu/Nel8X5XPfhtbVrze5z4iIyBlXy+u2TRtyz9rV39lbPvP9xe8uunCPyWiGYVS6hhIoukZ//VyZ7zmub3rMc1lExPbCG8EtU141VAqL1snlE7M+SurdVBHbjhfTc/bAgaf3178YbLsr2v2PDS/nXuTW/sqrnfasfWX55shrD5fm6W3FFvnon2Z9lHH9F/s8V0REbB370nP2wMFn9tftC26uib75coM399Vhu+9o51pJLNN3JZqWtUR+/7elGdfqX8vLWkmONCX9qAxukM78tZBy2GHIzS9KdKyJC+FtB4PpOXvAvXD/3wSD2+qjx5wNu3MvuGjvHet8JOsIg7syW27VgnvUW6Mn6Oeh0gR99SlN0FefOQz6OVXKoK8mKgY9n4wFAMUR9ACgOIIeABRH0AOA4gh6AFAcQQ8AiiPoAUBxBD0AKI6gBwDFEfQAoDiCHgAUR9ADgOIIegBQHEEPAIoj6AFAcQQ9ACiOoAcAxRH0AKA4gh4AFEfQA4DiCHoAUBxBDwCKI+gBQHEEPQAojqAHAMUR9ACgOIIeABRH0AOA4gh6AFAcQQ8AiiPoAUBxBD0AKI6gBwDFEfQAoDiCHgAqJKrb7Xq0/PdD0AOA4gh6ACi9zMl6+veobtcSXIFCXTN2CrgK9LxVBD0AlJ6luVV6Tifi+3SPtDZbJKo/2dMaMQzD6G/v6pssv6O6ff/SiGEYhtEvTbOP+ttne4Aqt2hdpSsojxVbKl1Beaz+daUrKI8dRqUrKI8F91S6gipmce9ttJ+Out1yuqdxb9AiIu7gSd2uaSEREdvSqDiK7RsZDIW6GjRPYsu2NCoOy2xqUT3oPw9WuoIyuNcuf3mt0kWUwfefkpe1ShdRBjsM+eifKl1EGaz+tfz1y0oXUQbfvqtUR3JsbGw6ElginsaNhohIwKU1Sb9hOCTg0vYX2iMyGJKliV/b+43Ook8EM8XSDQCUh2Nj+0Bf30D7xnRiJ34N9HXl9AwNRkQkOjSQ2GxYauvan17Vn/3SDUEPAGXi2NjY1dWYynnHxvauJk3TtD5pT0a7iIhY3HsTNzw52GhLtgT7Gz0NmqY1eBr7Zz+z1wxDhdXD8fHx/MaamhqWbuYTlm7mF3WXbvLzpKampiK1lAozegBQHEEPAIoj6AFAcQQ9AFS1qG7Pu/Am9Rnb6V2RQ9ADQPUKuLQGTyiv9UjiM7bT/Nys6h+YAoDKmvJassk+OB2QjUZkqf1Idmt0SFqbLSLi2Ni+f2jqz80S9ABQBv/3f0j+ov376fb8h/+dd5vD4ZDoUG5rZHBAmmdQC0EPAGWg/Yep+9yqhqWNeeE/GdboAaAMbvv3M/6ZNsuS5DdjBvq6GpdM/X1nBD0AlIH272b8M7WAK/F99Y6drT0NmqY1ybS+IIGlGwAog9tKlq4Wd7Az+aujM+hItRnuaR+BoAeAMtCqKF2rqBQAUMe0lmLmCEEPAGWgfavSFUwg6AGgDEq3Rj97VVQKAKiDGT0AKO42gh4A1MaMHgAUx+WVAKA4rYq+d4CgB4Ay4KobAFAcM3oAUBwzegBQHDN6AFAcl1cCgOIIegBQHJ+MBQDFsUYPAIqrphl9keecqG7XNE3TNFcg1RRwZWwAACajfWvGP2VTMOij+pOexn7DMAxjY59GvgPATFVT0BdcuokMhto3Jv4LtI5Owwi4NJcYG8tXBACopprW6AuW0rDUNjAUTW86Oo2Nffb9A3NWFADMd7d9a8Y/5aulUKPFfbK1pyFzycbRubcxVL4qAEAx1bR0U+TFhcUdNPqlKSvqDaPTUb5CAEAlpZnRpy6MyX2rNH3BjF2PFtovp5ZSDAgAkE27bcY/+QJHelojhpEz7xaRyGDygpmg2zJ1LZMEvWNje1cfF9wkjeqbHtAWPaAtesB+YvSW+ozqmx5wnS1vlTPxlf7zLdqKLdqKLfbur4r0ueJakeyjrTgZmLp93tCPiuuzShcxM+P6L57RrM9o1mfsb44X6TPosib7aNY3AgXbDw7OSbUzNaL/8G5twd3agrvtx0aK9DnjWpDsoy3YGZi6vdKajyZ/plyxSffMEx2S1maLiDg2tme+cSrRoYGupmlP6CcL+kBflyQOlfKNvc5yVN+02fM3LxnXPzSuv9T4XzcXyfFJ+ozqmzZ7Ls1dxVP5Sv/5bk9Dh3H5hHG5o/Gl3YWy/oprhXfg2YPG5RPG5RORZ+NNyUwv1j5v6EfFM1LpImZmXP/FPo9lmxE+aoS3NXr3Fcr6QZf12EDHPiN81AgfjXSMNSWzflz/xbGuHyf23eeLHiv+PFEpI/oP13i+223cvGHc7G58bk2hrD/jWtA2cOiCcfOGcfNG5NCnTclML9ZeBQIdyZ8pJ/Lpnnkig0UugokMii9iGIZxUo5MI+onm9F3Gjm+qWv0I6GeS40+1xoREVnT+c/O0Duh3D9t8T7RE7/SFm32iLN95VwWPanYxz2fWHxPLBMRkWWdr/wg9O7HuSP68HKX/GBv252JLUvbo+0SH4oVb58Poh+I9qx4RNrrKl3KjIwO9Fyp9z2+VERElnb67KH3B3LP1/mPu8S+d3NNYsuy2dEuY0OjIvLV4BVp/0Fi35rmDfWhkWIv4Crk2rs9H63yeR4SEZGHOt/6WeiP7+aO7r1Al/xs77a6xJZl2z+0y6dD14q3V4NSrNE3LG0sfHBHZ3LJpuhTQXYtk9yWXu0v5Yw+7HV7vW6n0+n0hkVE4r1up9PpdLp74xLvdXvDIvFet7s3LhL2unvjEvY60x1Ewl63253aV1al1BQy+2KThkdCKx9qrktt1tfZLo1EZtDnof7rHxpvtS0tWUGzdn0stPw/NZtTm4sW2j4Zyx3RA08al5+ceGqP3RiYvH2e6H9JjGekis7FdMS+CC1b2Vyb2jTfY7vyRe75WvuYEX5s4ryMfpU6L3cuXSZdf06s2Iyffn/YVndnueudmauR0Gpn8+LU5n0Nto8iuaN7+Ihx88jE6K5FByZvn53ShEkprrqxLJGe01ERCfR1NS6ZWIyP6vZ0HGe2FzPJd90EjnjEFzGWHNH6Nhqd4tL6NpZkRj88Yj7u95vCXndvvFYOn1t33K+b4r3uw71rd62T83ERqauLnY/HRdatld7DssfvtyZjXzfLcF2bX7cmDnXx4sXEL+PjBV6Nlirro8PXROqy264NjYijblp9LFtap/FmyZyKfh4XWZjdFh+KicNcuL/IV/p/eSP0k45gbodi7VXK8qBU27mYjuj1MZF7stvGhkbFUVu4v8i4vq839ONtwVoRqXH//uiSg89oVhGRdt/R4NqyFjtj0cinIg3ZbZ8OXRPH4sL9RUb0X74Qeqo7mNuhWPuM5efJrYRJSa6Ld+xs3d+geUTa+w2HiARc9qGdQbfFfXKpXdOaRNr7jc6pDzP5l5o1LrGkPj3l3tje1BfodMw+6+vXrTWJSK1ZQqOjIutaTSJiWrtOekZNNjl3PrxObLbEbeaW0dDw2VPO5FuY621ilnpz0Uc3yiO1oL9n2fTaUVmpBf3didct6bX7GhEJHHxG+3P6pvkotaD/ykPTa6+c0nwy1uIOGu6JTUdn0FGofQqTlJJMeMuSxtBg7gup2RiOjYqIjMbEXFtrlnPn4yISP39OzLVitdWd645Jba1ZurvFZpVac/36Pf6kDmsJy5hE9MSvEhfPaIse0H5zwVKfP0NYvKQua3s6faqH5V5TXptpScFZeew9+4rdPY8czE3zYu0oA8uihXltC5cUnPCM/pvduq9nw76JKD//cdeylpOptXvH4y22PwX0YheOzYnosR9p6etktp+xNHwnr8t3lhSclV/rsi9Y0/OTC7lpXqy9sqrpA1OTzOgt7r2N2pN6c3Cnb3+DponNF5nGS4Sp1Y90O50HRNbv8ZtMsmude6vzlEj9E8d1k4jJVncgZDaZRIbFXCtiamkTp9OZ2PGJ4/qcLBFYtvx3Y0vG9sjniQX35Kv+4ZHQyrqcl5rpRfnJ+lSP1KJ8strrY6HlCwtU++FJbXvc9y8n3ObptaNMUovyyfMV+yK07J4C5+v8G5pnzPfHo+7qftFr2faOsS1j+1o0sSifHN3VSGh1Q4HRvbdT2/Sp7+Mb7sXTa6+4avqum4JBH3BpTV02XyTYaThERGb0GmFq63b59fSM0tSi+1sybrR2+K0iIsn/E7F2+P0Zlx116HM0rc9QZ2td+UrTbx40/nGNyAXXz/22376Zu9Q7nT7Vw/y91uVvNB1YYexZJnLFtf3PtmcP5lYbe8++Pe77l+dz07xYO8qntrF1WW/Twe8Zu5eKDLo8QVvHvtzzNfpvds+Y7487clN+7ffaPceefLMxuLlGRAKv94aWtZysqmeCxY+0rn6habvDeOUhkTOuTX+wHbqQO7prXfZNn/o+fic3zYu1V4Nq+j76gkHv6DSMzqhu17SQtPd/Y6+qnFDrfutN2bRZWyQiYvvtm8EtyX8ogd88sL8hsVm0T1W60/3PB+Xnu7UVIiK2Zw8GU5dLBg5s2V9/MNh2Z/Tc/xuSaOg/b/Fk7Nb+yomdnxdu73xg7qr/5qlx/36f/GJf4g1VW8e+YGopJnDwmf11+4Kba6IfXgrJcOgnz2SdF9/RzrVLO8PbXNZ9mldERJa1RH7/t1U2Balz/z8X5IdrtAUiIrZDF4KpyyUD2+/ef/+F4La66L/6Q3Ix9L27s0b31o2dkcLtnQ/PWfHFVdOMXjMMY9IOAZfW1CXVnvdFr7r5PDj3xZTdvXb5y2uVLqIMvv+UvKxVuogy2GHIR/9U6SLKYPWv5a9fVrqIMvj2XaW56ubDQzPe5YHnZrzL9Ez5nJP82NTGvm/yJ2MBYIaq6c3YqV9cJD421bfxG/zJWACYqWoK+sk+MJVYtLH5IoZRZWt6AFDlbquiNfqCpQRcWmoSP80vwQQAZCrJ1xSXSPGrbsp3nwCgvHIuxczU5F+BAAC4JdV0eSVBDwBlUPUfmAIAzA5LNwCgOGb0AKA41ugBQHEs3QCA4pjRA4DiWKMHAMUxowcAxTGjBwDF8WYsACiOoAcAxVXT1xQT9ABQBtU0o6+i5xwAUEdpvo8+8Z/4y/8PuRZrL4ygB4AyuO1bM/7JFzjS0xoxDKNfmrIivVh7sVpKNSgAwIS6v03+TDmRT/fMEx2S1maLiDg2tg8MRadsL4Y1egAog89DyV+0qWI23fNeW84tkcEBaS6wR7H2YpjRA0AZ3HbbjH/yNCxtLHjsYu1Fa7mVAQAAJqd9a8Y/eSxLpOd0VEQCfV2NSyxTthdD0ANAGZTkqhvHztaeBk3TmqS/0yEiAZddjxZonwJr9ABQBqX5rhuLO2i4JzYdnUFHofYpEPQAUAZ8eyUAKO62KkrXKioFANTBjB4AFFdN33VD0ANAGTCjBwDF8V+YAgDFsXQDAIpjRg8AimONHgAUV01LN5phGJWuoQTGx8fzG2tqaua+EgDzXX6e3EqY/K//b8a7/B//cca7TI/qM/rRcKUrKINaq7ysVbqIMthhyF9eq3QRZfD9p+TyiUoXUQYrtshfv6x0EWXw7btKc5xqmtGrHvQAUBEEPQAojqtuAEBxXHUDAJ+x5cEAABUhSURBVIpjRg8AimONHgAUV01LN1VUCgCgHAh6AFAcQQ8AiiPoAUBxBD0AKI6gBwDFEfQAoDiCHgAUR9ADgOIIegBQHEEPAIoj6AFAcQQ9ACiOoAcAxRH0AKA4gh4AFEfQA4DiCHoAUBxBDwCKI+gBQHEEPQAojqAHAMUR9ACgOIIeABRH0AOA4gh6AFAcQQ8AiiPoAUBxBD0AKI6gBwDFEfQAoDiCHgAUR9ADgOII+mka1Teu0cxrNPMa+6ujM+wz0a7tPj8XxZaOflRcn1W6iJn5Sv/5Fm3FFm3FFnv3V0X6XHGtSPbRVpwMTN1eDb7Un3paW/m0tvJpe/eXRfpcca1M9tFWZtY/sa924MqcVDtTI/oP79YW3K0tuNt+bKRInzOuBck+2oKdganbMYGgn45RfWOL536vEbtgxLyN+1oKZX3RPoHd6fZe32cdxZ8nqo5+VDwjlS5iZr7Sf77b09BhXD5hXO5ofGl3oay/4lrhHXj2oHH5hHH5ROTZeFMy04u1V4Mv9aee91g8xqVXjUuexpefL5T1V1wrfQM7XjQuvWpcejWyY6wplfWBA+l9X/RFfcWfJyplRP/hGs93u42bN4yb3Y3PrSmU9WdcC9oGDl0wbt4wbt6IHPq0KZnpxdqRpcqCPh6Pi0jY6+6NV7qUDCPBnkvLfL9aKyIiaztP/Tj0djA6zT4j/3P//0i31zY/uqzAvtUn+oFoz4pHpL2u0qXMSOzjnk8svieWiYjIss5XfhB69+Pcv/aHl7vkB3vb7kxsWdoebZf4UKx4ezWIfZI9rgdD732SO67g5S55cG/bXYktS9uP2mVsKCYSe2//W+l972p+2FJg38q69m7PR6t8nodEROShzrd+Fvrju7kVvhfokp/t3VaX2LJs+4d2+XToWvF2ZLu90gVkCnsPx1r1FrF26NZK15JpeCS0csPJutRmfZ3t0khExDKdPnU/DcZ+mmodPf32Fdujv83asVr1vyQOEf2oDFa6khm4PhZa/p9OmlObixbaPhnLPVMPPGlcztiM3RgQWTpJezX4fCy0fNXEuO4tNC77k8aljM10/eaHg5ceTrV+efq9qO3hLdX1CLwaCa12nlyc2ryvwfZRJHd0Dx8xbmZsXosmR1esHdmqKOjjvd1nh4fPes1+W8gda9XNPe6QyNmzw7J+j7/DKmGv88BZEal/4rjeYhIRkVWrViX2vXjxYvkKi16LitTltA2NiKNu+n1G9Y0tnksiKzsiT9eWr9RSsTwo1ZUF0xP9PC6yMLstPhQTh7lwf5Gv9P/yRugnHcHcDsXaKyN6fSxvXGOTjutL/YU3Q5s8GfV/qT/1vOcTkeWbI6lZf5WIRj4Vachu+3TomjgWF+4vMqL/8oXQU93B3A7F2mespqZmtoeoMlUU9KaWtvXnYq0dVgmHEi3DI+bjfr8p7HX3xmulW/b4/dbkwk4i6tP5Pj4+nn/Aajpbte6+C24ROXtIMx/qjz3nqHRBmFjQ37Nseu3zRWpBP6v+u9yvveoWkeBJbeXJ/ktPzttHYGpB/5WHptd+K/LzpJrC5FZUUdDnq1+31iQitWYJjY7K8NlTzrOJG9bbREzlu9/oq79s2Je6OOHvvcb/mT+7tSypy95ePHUfEZH1f+9b2dJ39jnH+hLUiRyWe/MfFaYlBae9sffs//kNefagkVqUn6K9oiyLFua1LSw6ro1vyo4XjWLTdvvf+ZY/3xd80mEvaYkzET32o4bnUi/Bn+o2Hv1OXpfvLCk4K7/WZf/eC3LogpFalJ+iHSlVHfTDsVERk4zGxLy2VurX79E75mTt3vL074ynM7ZHPs9alB8eCa2sy3mpmbtwn+5z9pD2hGRM4WODlwTlkrMof30stHxh7pkSkQ9Patvjvn854TZPr73ichblPy8yruBJbfuYr+/VrPqDJ7XtkjGF/2rwk3KXOwXLtneMbRnb16JZi/JXI6HVDQVG995ObdOnvo9vuBdPrx0Zquqqm1qznNrqDae360e6nU6n84C0tZhMLW1ywJkw19fk1NlbV/6pKXkJ/HnXE3+yPWrPncAX67P+730r/9R3NtXt7AddKzt2Mp0vE/P3Wpf/uSl5qfgV1/Y/2x75Xu6Zir1n3x73/cvzuWlerL0amJe3Lv8gY1wf2B5eXmhcY76+3+TWb/873/IP+oKpzeDlruWbd1ZuOl/A4kdaV/+hafsZERE549r0B9tPHskd3bUu+6ZPfR+/k5vmxdqRTTMMo9I1FBH2umOtqbddp1B0jX40nN9+S1LvporY9vUGU2+oBnav2d+Q3izcJ7NdVnZE+n462/c5a63ysjbLY0yTflQGN0jn/XNyZzsM+ctrsz7KV/rPd3s+ERGxPXswmFqBCRzYsr/+YLDtzmj3iw0v5V681/7KiZ2fF27vfGDWFX3/Kbl8YtZHSb2bKmLb8WIwtTITOPD0/voXg213Rbv/seHl/Ppf7bRn7SvLN0dee7g077Sv2CJ/LdUl+SP6D9d4PhIRsR26EEytwAS2373//gvBbXVZqz0p7W/d2Bkp3N75sNy6b9+l3ho9QT8PzWHQz6nSBH31KU3QV59SBn01UTHoq3iNvtqupgeA+amq1ugBAKVH0AOA4gh6AFAcQQ8AiiPoAUBxBD0AKI6gBwDFEfQAoDiCHgAUR9ADgOIIegBQHEEPAIoj6AFAcQQ9ACiOoAcAxRH0AKA4gh4AFEfQA4DiCHoAUBxBDwCKI+gBQHEEPQAojqAHAMUR9ACgOIIeABRH0AOA4gh6AFAcQQ8AiiPoAUBxBD0AKI6gBwDFEfQAoDiCHgAUR9ADgOIIegBQHEEPAIrTDMOodA0lMD4+nt9YU1Mz95UAmO/y82S+h8ntlS6gzF7WKl1BGewwZDRc6SLKoNYqnwcrXUQZ3GuX6+cqXUQZLFpX6QowXSzdAIDiCHoAUBxBDwCKI+gBQHEEPQAojqAHAMUR9ACgOIIeABRH0AOA4gh6AFAcQQ8AiiPoAUBxBD0AKI6gBwDFEfQAoDiCHgAUR9ADgOIIegBQHEEPAIoj6AFAcQQ9ACiOoAcAxRH0AKA4gh4AFEfQA4DiCHoAUBxBDwCKI+gBQHEEPQAojqAHAMUR9ACgOIIeABRH0AOA4gh6AFAcQQ8AiiPoAUBxBD0AKI6gBwDFEfQAoDiCvjT0o+L6rNJF3IpRfeMazbxGM6+xvzo6wz4T7dru83NR7AyM6pse0BY9oC16wH6i+Lgm6zOqb3rAdba8VZZOXN/0oFb3oFb3oP1EvNLF3IKobtcS7Hp0yj6aK5B5Q8CVap9k7282gr4E9KPiGal0EbdiVN/Y4rnfa8QuGDFv476WQllftE9gd7q91/dZR/Hnibk3qm/a7Pmbl4zrHxrXX2r8r5uL5PgkfUb1TZs9l+au4tmJ65se89x/xBj5wBg50vjfHptvWR/V7Q2exn7DMAyjv9HTUDCtA650n4hvoCndJ+DS9i+NGEZ67+wnAYjIvAz6eLx6HsXRD0R7Vjwi7XWVLuUWjAR7Li3z/WqtiIis7Tz149Dbwdx/YcX6jPzP/f8j3V7b/OiyAvtWykio51Kjz7VGRETWdP6zM/ROKG9cRftET/xKW7TZI872lXNZ9CxcD/VcbvS5rCIiYu187dEC461m0dM9IZtvp0NERByd/e2hntO59Uf1/V3pPpbmVluyT1Tf32VrbbYkuzk6DaPTMWeVzx/zLujD3sPVtUrQ/5IYz8jSSpdxK4ZHQis3NNelNuvrbJdGItPsU/fTYOx37mT76Om3r9getVukOgyPhFY+NI1xFevzUP/1D4232ubNOb02ElqxvnlRanNxne1y3nirWWQwlJHV0rDUFhrMrd/iDhpBd7JP9HRPao+cfVHE3AR92OtM8IZFJOx1e73u9KbEe90Zt6Y33b3xvH3jvd1nh09tTXQUWZVSU8gcDMzyoMzf6UP0Wv60Lzo0MqM+iWX6Fo90nHy6ttQF3qLo8LW8tmu54yrex7KldX6d0+jwcF7b8ND1ClRya6JDA3ltA0OFX5IklukbPOI76bak940UW7y/VRUJk7K6fQ7uI97bLXv8fqukl12GR8zH/X5T2OvujVvNPefWHffrJgl7nd7wcXN3YjPe6z7cu3aXZO1ramlbfy7W2pF4lSoXL15M/DI+Pp5/vwqcnqpX6+674BaRs4c086H+2HPzKyIx31jcQcMtibdfXf1GZ4OIhDz7WyOGYZGM9lk+DvPzZL6HyVzM6E0tbXIgMStPLrvUr1trEpFas8RG47GROrNJRMTa4e+wjsYkcaNp7TqJjebvi1sWffWXyYtkzGu03ecti/Nf8VqW1GVvT6OPiMj6v/et/FNfha5RiZ74VeLiGW3RA9pvLljqF+d1WZw7rmn0mS8s9fV5bfVLFhXoWSUyrp7RNFfAsqQxr0vjkslXYxw7fbauvsTs3Zac3ee2I8PcLN1YO/x+v9/v31N37nxcRIZjoyIiozEx15rMdSOxuIhIvNft7hWzJPrEz58Tc23+vrhllqd/Z8QuJH8Ors1dvB4eCa2sa8jZp1ifs4c086GMf1Cxwcpdo2LZ8t+N6x8mf/5xzazGNR/lLMpfGwmtqOqxWNxBI63TkbsoHxkM2Zbm1h9wZS/LRAZDIiKFniRQwFwEfXoN3nlA2lpMIlI/0j2xae1oi211Op3OrefW7Wqxtuxad25rasuUt2+tWSbW6DErdfbWlX9qSl4Cf971xJ8KvKFarE/OFP7sB10rO3aun6vKJ1dna13pb/rNBRERueD6ud/2I1veuKbRZ75YZGtd8XbT84l/FGHXU2/Ps7FYmlttXU3JGA+4mroKvL2aM1UP9CWvwXHs9InnyET7EU+ofSMLiHk0wzDm+j7DXnesVW8xlfCQRdfoX9ZKeC+T0I/K4AbpvH9O7myHIaOleqob1Te2JC4Yt+3rDabeUA3sXrO/Ib1ZuE9mu6zsiPT9dLbhUmuVz4OzPEbKxIXwtt++GdySGtdvHtjfkN4s3CfzCIP/14eds3/2utcu18/N+iiTi+ubHvNcFhGxvfBGcEsp/3EVtWhd6Y4V1e0NnpCIiM0XSV9ek7hGPrU50SerU2a7tM9+gV5UXKMn6OehUgZ9NSll0FeTuQj6Sihl0FcX9YJ+Lq66yWXt0K0VuFsA+Gaadx+YAgDMDEEPAIoj6AFAcQQ9ACiOoAcAxRH0AKA4gh4AFEfQA4DiCHoAUBxBDwCKI+gBQHEEPQAojqAHAMUR9ACgOIIeABRH0AOA4gh6AFAcQQ8AiiPoAUBxBD0AKI6gBwDFEfQAoDiCHgAUR9ADgOIIegBQHEEPAIoj6AFAcQQ9ACiOoAcAxRH0AKA4gh4AFEfQA4DiCHoAUBxBDwCKI+gBQHEEPQAojqAHAMVphmFUuoYSGB8fz2+sqamZ+0oAzHf5eTLfw+T2ShdQRgXTv0xqamrm8u7mDOOaXxgXCmLpBgAUR9ADgOIIegBQHEEPAIoj6AFAcQQ9ACiOoAcAxSnygamKW7Vq1cWLFytdRekxrvmFcaEgZvQAoDhm9ACgOGb0AKA4gh4AFEfQA4DiCHoAUBxBP4fi8XilS5hMoryw191b1WUiKefhlDhxU56+Sj0ICxY2zQdbulvB4nMOwgO4EIJ+zoS9h89XuoZJpMqzdugtpkoXgynlPZymdeIq9yCczeMquW+V/wuqagT95MJeZ4I3LCJhr9vrdac3Jd7rzrg1vZmcUWTtG+/tPjt8amuiY/WZKC89McwaaWos82KyVOw0uXvjEu91J06H290bn5gGZ44u7HW73al9q0b6sZYcRsb5SlSbnsmeO5z1iO2NJ/b2hiv6IMz+S0/8eWM9RR9m2UObKD77T5E75PT9zaNHbPmp/F+Ymr14b7fs8futkn7RODxiPu73m8Jed2/cau45t+64XzdJ2Ov0ho+buxOb8V734d61uyRrX1NL2/pzsdYOa2VHVMxEeeFQoiVzpLXpsYS97t549U/5s4s/nHVe1sn5uIjU1cXOx+Mi69ZK7+Gs0ZlluK7Nr1fbmVq7y99iEon3untGpSXjfCWrDXu7RUQkvenujVvNWYeoigfh+j3+Dmu8190TFmvqTEmvuycstbHuwici7O3OLD5em/mnkEJDnviXO08eseVG0E/G1NImTqdTRKT+ieN6i0j9urUmEak1S2g0LiN1ZpOIiLXDb5Wwt3tdq0lETGvXSc+oqSN33/klc6SjMnz2lPNs4ob1NpFq/2eTXbxknRebnDsfXic2W+I2c8toKHt0Zqk311ay+oJMct7tPDUsIrJ+T9YtOdUmN2vNEhqV7KCvBrnVrltrEklMukdj0zsReX+K/CHnHqrqH7HlRtBPztrh93eISNjrPh9vMctwbFTEJKMxMa81metGYnGxmiTe6z4sbevk3Pl4S4spfv6cmHeJmLL2nW9JnznSWqlfv0ev1lcjBWQXL92Z58Vqq+vujq3btdYsh7ulTZfaWPboUq9pqkrYe1h2+f0mCXudk9eXOfZ042hsuApDP0eteVonIv9PkT/k3EN94xH0k4n3urcmZg6yfo/fJGGpH+l2Og8kN6WjLZSYtNc/cVy3mtbG3FudpxJbptx9RcxyYKvX7K/SR19tsjxbcjtzpCZJvzpJvDyp9vlRdvG71mWcFxGTre5AyGwyiQyLuTbrdZvUP3Fcr8pAtNrqDmx1nhJZv379SCwu1tzzlZb9EE3uVl9fX2eTibNclQ/CqU5EesjZfwpz9pDDhQ5V9Y/YcuO7bmYi7HXHWr8RD5p5PdJ5XTxQBlx1AwCKY0YPAIpjRg8AiiPoAUBxBD0UE9Xtml2PTtFUVMCluQJlKQyoGIIeirG497aHek5nxHr0dE+ofa/bMvW+AZfW1FW+0oAKIeihHMdOn2QkffR0j/h2OkQSU/uExKw9qtvtLpc9sR3V7X0bjf72SpUNlA1BD/VYmlvFcyS5ABM44pHWZouIRPUne1ojhmEY/e1dfcmbQwNLTxqG0ekQizvY6ahYzUAZEfRQkMW9NxXlgb6u1LKNxR08KU9qmqY1dcnAUGLKb0s8CQAqI+ihJMfG9q6+gET1/V3tG5Pz9IBLaxjcm5jRV7Y6YG4R9FCTY6dvYL/rSHp5PiER+oE+3nHFNwpBD0VZmlulq0syVmYcG9u7mjRN0/qkXUKDkUpWB8wlvgIBABTHjB4AFEfQA4DiCHoAUBxBDwCKI+gBQHEEPQAo7v8HxF7RiSHu2LEAAAAASUVORK5CYII=) 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Visualisasi 2
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#Bila data Beda Satuan, perlu penskalaan data
datakuskal <- scale(dataku)
head(datakuskal, 10)
## spcost tipe power atribut inheritable
## [1,] -0.829012192 -1.9516720 -1.2925274 -1.2586863 1.6291575
## [2,] -1.198600502 -0.3412237 -0.7592543 -0.9075059 -0.6125987
## [3,] -0.829012192 -1.9516720 -1.2925274 -1.2586863 1.6291575
## [4,] 2.312488435 1.2692246 -1.2925274 -0.5563254 -0.6125987
## [5,] -1.198600502 1.2692246 -0.6525996 -0.2051450 -0.6125987
## [6,] -0.921409270 1.2692246 -0.2259811 0.1460354 -0.6125987
## [7,] -0.459423883 -0.3412237 0.6272560 -1.2586863 -0.6125987
## [8,] 0.464546890 1.2692246 0.2006374 -0.5563254 -0.6125987
## [9,] 0.002561503 -0.3412237 1.1605291 0.4972159 -0.6125987
## [10,] -1.013806347 -1.9516720 -1.2925274 -1.2586863 1.6291575
#Penentuan jumlah Klaster dengan pendekatan klaster gap untuk memperoleh K terbaik
library(cluster)
gapclust <- clusGap(datakuskal, FUN = kmeans, nstart = 30, K.max = 20, B = 10)
## Clustering k = 1,2,..., K.max (= 20): .. done
## Bootstrapping, b = 1,2,..., B (= 10) [one "." per sample]:
## .
## Warning: did not converge in 10 iterations
## .....
## Warning: did not converge in 10 iterations
## ...
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## . 10
library(factoextra)
fviz_gap_stat(gapclust) + theme_minimal() + ggtitle("Visualisasi Statistik Gap Klaster") #diperlukan 2 Klaster
![plot of chunk unnamed-chunk-34](data:image/png;base64,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) 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Visualisasi 3
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#Penentuan K optimal dengan PCA
library(FactoMineR)
pcaku <-PCA(datakuskal, graph = F)
fviz_screeplot(pcaku, addlabels = T) #Untuk dapat menjelaskan ragam sekitar 80% diperlukan Klaster = 5
![plot of chunk unnamed-chunk-35](data:image/png;base64,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) 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Visualisasi 5
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#Penentuan K optimal dengan pendekatan Silhouette
fviz_nbclust(datakuskal, kmeans, method = "silhouette", k.max = 24) +
theme_minimal() + ggtitle("Plot Penentukan K Optimal Metode Shilhouette") #diperlukan 2 klaster
![plot of chunk unnamed-chunk-36](data:image/png;base64,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) 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Visualisasi 6
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#Penentuan K optimal dengan pendekatan NbClust
library(NbClust)
nbclust <- NbClust(datakuskal, distance = "euclidean", min.nc = 2, max.nc = 20,
method = "complete", index = "all")
![plot of chunk 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) 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Visualisasi 7
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## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
##
![plot of chunk unnamed-chunk-37](data:image/png;base64,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) |
Visualisasi 8
|
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 10 proposed 2 as the best number of clusters
## * 2 proposed 3 as the best number of clusters
## * 1 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
## * 1 proposed 8 as the best number of clusters
## * 3 proposed 9 as the best number of clusters
## * 1 proposed 15 as the best number of clusters
## * 2 proposed 18 as the best number of clusters
## * 2 proposed 20 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 2
##
##
## *******************************************************************
fviz_nbclust(nbclust) + theme_minimal() + ggtitle("Plot Penentuan K Optimal \n Metode NbClust") #diperlukan 2 klaster
## Warning in if (class(best_nc) == "numeric") print(best_nc) else if (class(best_nc) == : the condition
## has length > 1 and only the first element will be used
## Warning in if (class(best_nc) == "matrix") .viz_NbClust(x, print.summary, : the condition has length > 1
## and only the first element will be used
## Warning in if (class(best_nc) == "numeric") print(best_nc) else if (class(best_nc) == : the condition
## has length > 1 and only the first element will be used
## Warning in if (class(best_nc) == "matrix") {: the condition has length > 1 and only the first element
## will be used
## Among all indices:
## ===================
## * 2 proposed 0 as the best number of clusters
## * 10 proposed 2 as the best number of clusters
## * 2 proposed 3 as the best number of clusters
## * 1 proposed 4 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
## * 1 proposed 8 as the best number of clusters
## * 3 proposed 9 as the best number of clusters
## * 1 proposed 15 as the best number of clusters
## * 2 proposed 18 as the best number of clusters
## * 2 proposed 20 as the best number of clusters
##
## Conclusion
## =========================
## * According to the majority rule, the best number of clusters is 2 .
![plot of chunk unnamed-chunk-37](data:image/png;base64,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) 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Visualisasi 9
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#Penentuan K optimal dengan fungsi Clustree
library(clustree)
coba <- NULL
for (k in 1:16){
coba[k] <- kmeans(datakuskal, k, nstart = 30)
}
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
## Warning in coba[k] <- kmeans(datakuskal, k, nstart = 30): number of items to replace is not a multiple
## of replacement length
df <- data.frame(coba) #Menambahkan identitas kolom
colnames(df) <- seq(1:16)
colnames(df) <- paste0("k",colnames(df)) #PCA setiap amatan
dfpca <- prcomp(df, center = TRUE, scale. = FALSE)
inkoor <- dfpca$x
inkoor <- inkoor[,1:2]
df <- bind_cols(as.data.frame(df), as.data.frame(inkoor))
clustree(df, prefix = "k")
![plot of chunk 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) 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Visualisasi 10
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#Penentuan K Optimal dengan menggabungkan beberapa metode
library(clValid)
bandingmetod <- clValid(datakuskal, nClust = 2:24, clMethods = c("hierarchical", "kmeans", "pam"),
validation = "internal")
## Warning in clValid(datakuskal, nClust = 2:24, clMethods = c("hierarchical", : rownames for data not
## specified, using 1:nrow(data)
#Ringkasan Beberapa Metode
summary(bandingmetod) %>%
kable() %>% kable_styling() #Terlihat K optimal adalah 2
##
## Clustering Methods:
## hierarchical kmeans pam
##
## Cluster sizes:
## 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
##
## Validation Measures:
## 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
##
## hierarchical Connectivity 4.8579 15.8940 16.8940 16.8940 16.9940 32.5389 32.5389 38.7619 40.0119 43.7357 62.5472 62.5472 62.5472 62.5472 63.8806 67.2270 68.0865 71.9556 78.5524 80.0397 87.8567 94.0504 100.4496
## Dunn 0.2482 0.1750 0.1750 0.1862 0.1862 0.0866 0.0866 0.0954 0.0954 0.0954 0.0813 0.0879 0.0879 0.0917 0.0917 0.0917 0.0917 0.0917 0.0917 0.0917 0.0917 0.0577 0.0577
## Silhouette 0.5743 0.3289 0.2953 0.2774 0.2460 0.2394 0.3051 0.2925 0.2900 0.2810 0.3036 0.3180 0.3226 0.3390 0.3372 0.3435 0.3439 0.3447 0.3418 0.3422 0.3459 0.3679 0.3747
## kmeans Connectivity 31.0258 46.9234 45.1480 55.2615 37.2889 105.3278 112.9909 102.8933 106.8726 95.4925 115.8099 120.0135 120.0135 108.1873 92.7317 112.0877 117.6790 126.4944 137.0829 133.7988 129.8329 124.6083 134.9381
## Dunn 0.0113 0.0230 0.0254 0.0345 0.0346 0.0222 0.0445 0.0223 0.0228 0.0457 0.0502 0.0243 0.0243 0.0640 0.0640 0.0321 0.0643 0.0321 0.0322 0.0330 0.0323 0.0340 0.0366
## Silhouette 0.3221 0.2846 0.3310 0.3221 0.3199 0.3219 0.3262 0.3249 0.3422 0.3534 0.3228 0.3280 0.3386 0.3544 0.3593 0.3932 0.3936 0.3845 0.3849 0.3912 0.3899 0.4118 0.4197
## pam Connectivity 2.3552 7.1548 19.3996 72.2540 80.6262 73.8270 114.4377 79.9421 87.7000 103.9385 116.2214 113.7968 114.0806 113.9278 122.1270 137.5746 142.7107 136.7690 138.3218 164.4119 181.7690 182.1857 176.5302
## Dunn 0.1856 0.1617 0.0428 0.0114 0.0139 0.0139 0.0132 0.0133 0.0141 0.0141 0.0143 0.0143 0.0143 0.0285 0.0302 0.0302 0.0302 0.0302 0.0302 0.0151 0.0151 0.0151 0.0302
## Silhouette 0.3087 0.2312 0.2857 0.2918 0.3037 0.3059 0.3169 0.3343 0.3389 0.3588 0.3504 0.3564 0.3607 0.3768 0.3695 0.3698 0.3787 0.3836 0.3916 0.3744 0.3664 0.3774 0.3756
##
## Optimal Scores:
##
## Score Method Clusters
## Connectivity 2.3552 pam 2
## Dunn 0.2482 hierarchical 2
## Silhouette 0.5743 hierarchical 2
#Menentukan Matriks Jarak
jarak <- dist(datakuskal, method = 'euclidian')
#Pengklasteran
set.seed(500)
hc <- hclust(jarak, method = 'average')
#Membuat Plot Dendogram
par(mfrow = c(1, 1))
plot(hc)
#Memotong Dendogram
abline(h = 2, col = "red")
rect.hclust(hc, k = 2, border = 2:6)
![plot of chunk 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) 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Visualisasi 11
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#Membuat Klaster dengan K = 2
hc_fit <- cutree(hc, k = 2)
#Tabulasi melihat jumlah anggota masing-masing klaster
table(hc_fit) #Anggota Klaster 1 503 Digimon, dan Klaster 2 hanya 2 jenis Digimon
## hc_fit
## 1 2
## 503 2
#Mengcustome Dendogram
library(dendextend)
denobj <- as.dendrogram(hc)
digimon_den <- color_branches(denobj, h = 1)
plot(digimon_den)
![plot of chunk 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) 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Visualisasi 12
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Demikian sedikit ulasan kita bagaimana mempraktikkan algoritma K-Means clustering yang merupakan salah satu jenis machine learning di era Big Data dan Data Science dengan R. Jangan lupa untuk menantikan unggahan berikutnya. Selamat memahami dan mempraktikkan!