{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMOceJDJDYI5M3GHowbGW9D"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# **Tugas 4 | Implementasi K-Means Clustering**"],"metadata":{"id":"IjAlnsiFkXvv"}},{"cell_type":"markdown","source":["## K-Means Clustering"],"metadata":{"id":"4Az2jUE2kjFA"}},{"cell_type":"markdown","source":["K-Means Clustering adalah salah satu algoritma dalam menentukan klasifikasi terhadap objek berdasarkan attribut / fitur dari objek tersebut kedalam K kluster/partisi. K adalah angka positif yang menyatakan jumlah grup/kluster partisi terhadap objek. Pemartisian data dilakukan dengan mencari nilai jarak minimum antara data dan nilai ***centroid*** yang telah di set baik secara random atau pun dengan ***Initial Set of Centroids***, kita juga dapat menentukan nilai centroid berdasarkan ***K object*** yang berurutan"],"metadata":{"id":"OLXLbUYJmcrZ"}},{"cell_type":"markdown","source":["***Centroid*** adalah nilai rata-rata aritmetik dari sebuah bentuk objek dari seluruh titik dalam objek tersebut. Penerapan K-Means Clustering ini dapat dilakukan dengan prosedur step by step berikut :\n","\n","- Siapkan data training berbentuk vector.\n","- Set nilai K cluster.\n","- Set nilai awal centroids.\n","- Hitung jarak antara data dan centroid menggunakan rumus ***Euclidean Distance***.\n"," \n"," Rumus Menghitung Jarak :\n","\n"," > $𝙙(p,q) = \\sqrt {Σ_{i=1}^{n}(q_i - p_i)^2} $\n"," \n"," ```\n"," ket :\n"," p,q\t =\tdua titik di ruang-n Euclidean\n"," qi,pi =\tvektor Euclidean, dimulai dari asal ruang (titik awal)\n"," n =\truang-n\n","\n"," ```\n"," \n"," \n","- Partisi data berdasarkan nilai minimum.\n","- Kemudian lakukan iterasi selama partisi data masih bergerak (tidak ada lagi objek yang bergerak ke partisi lain), bila masih maka ke poin 3.\n","- Bila grup data sekarang sama dengan grup data sebelumnya, maka hentikan iterasi.\n","- Data telah dipartisi sesuai nilai centroid akhir."],"metadata":{"id":"S6x4PUjSm3pu"}},{"cell_type":"markdown","source":["## Implementasi ke Bahasa Pemrograman Python"],"metadata":{"id":"jLzKGEdHrXGf"}},{"cell_type":"markdown","source":["### Persiapan data"],"metadata":{"id":"i03mCjd1reNz"}},{"cell_type":"markdown","source":["Data yang akan digunakan adalah data iris, yang dapat diperoleh [disini](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data)."],"metadata":{"id":"tJdD0y4WtTR3"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"jrFm_H2aFQko","executionInfo":{"status":"ok","timestamp":1664672349949,"user_tz":-420,"elapsed":387,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"colab":{"base_uri":"https://localhost:8080/","height":424},"outputId":"d942ca60-169b-4454-88fa-60f47de9e46c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" sepal-length sepal-width petal-length petal-width Class\n","0 5.1 3.5 1.4 0.2 Iris-setosa\n","1 4.9 3.0 1.4 0.2 Iris-setosa\n","2 4.7 3.2 1.3 0.2 Iris-setosa\n","3 4.6 3.1 1.5 0.2 Iris-setosa\n","4 5.0 3.6 1.4 0.2 Iris-setosa\n",".. ... ... ... ... ...\n","145 6.7 3.0 5.2 2.3 Iris-virginica\n","146 6.3 2.5 5.0 1.9 Iris-virginica\n","147 6.5 3.0 5.2 2.0 Iris-virginica\n","148 6.2 3.4 5.4 2.3 Iris-virginica\n","149 5.9 3.0 5.1 1.8 Iris-virginica\n","\n","[150 rows x 5 columns]"],"text/html":["\n","
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sepal-lengthsepal-widthpetal-lengthpetal-widthClass
05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
..................
1456.73.05.22.3Iris-virginica
1466.32.55.01.9Iris-virginica
1476.53.05.22.0Iris-virginica
1486.23.45.42.3Iris-virginica
1495.93.05.11.8Iris-virginica
\n","

150 rows × 5 columns

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\n","
\n"," "]},"metadata":{},"execution_count":942}],"source":["import numpy as np \n","import matplotlib.pyplot as plt \n","import pandas as pd\n","\n","url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n","#the imported dataset does not have the required column names so lets add it\n","colnames = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']\n","irisdata = pd.read_csv(url, names=colnames)\n","irisdata"]},{"cell_type":"markdown","source":["### Mengubah Label menjadi angka"],"metadata":{"id":"eSQzYHwZr7Xx"}},{"cell_type":"code","source":["irisdata['Class'] = pd.Categorical(irisdata[\"Class\"])\n","irisdata[\"Class\"] = irisdata[\"Class\"].cat.codes\n","irisdata"],"metadata":{"id":"TpsenppxFlel","executionInfo":{"status":"ok","timestamp":1664672350500,"user_tz":-420,"elapsed":21,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"colab":{"base_uri":"https://localhost:8080/","height":424},"outputId":"3f9b1766-da6b-40a4-d31c-935c258ffdb3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" sepal-length sepal-width petal-length petal-width Class\n","0 5.1 3.5 1.4 0.2 0\n","1 4.9 3.0 1.4 0.2 0\n","2 4.7 3.2 1.3 0.2 0\n","3 4.6 3.1 1.5 0.2 0\n","4 5.0 3.6 1.4 0.2 0\n",".. ... ... ... ... ...\n","145 6.7 3.0 5.2 2.3 2\n","146 6.3 2.5 5.0 1.9 2\n","147 6.5 3.0 5.2 2.0 2\n","148 6.2 3.4 5.4 2.3 2\n","149 5.9 3.0 5.1 1.8 2\n","\n","[150 rows x 5 columns]"],"text/html":["\n","
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sepal-lengthsepal-widthpetal-lengthpetal-widthClass
05.13.51.40.20
14.93.01.40.20
24.73.21.30.20
34.63.11.50.20
45.03.61.40.20
..................
1456.73.05.22.32
1466.32.55.01.92
1476.53.05.22.02
1486.23.45.42.32
1495.93.05.11.82
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150 rows × 5 columns

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\n"," "]},"metadata":{},"execution_count":943}]},{"cell_type":"markdown","source":["### Menentukan Attribute yang akan dipakai"],"metadata":{"id":"g01dViUUsFpB"}},{"cell_type":"markdown","source":["Pada kasus ini menggunakan `attribute` dari 0 sampai 4"],"metadata":{"id":"MFgEO2y1sQwV"}},{"cell_type":"code","source":["x = irisdata.values[:, 0:4]\n","y = irisdata.values[:, 4]\n","# delete 'variety' column\n","df_without_label = irisdata.drop(columns=[\"Class\"])\n","df_without_label"],"metadata":{"id":"640sNMGkFn3M","executionInfo":{"status":"ok","timestamp":1664672350501,"user_tz":-420,"elapsed":20,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"colab":{"base_uri":"https://localhost:8080/","height":424},"outputId":"178d1797-3233-4ff4-b278-38a6d8efb9c8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" sepal-length sepal-width petal-length petal-width\n","0 5.1 3.5 1.4 0.2\n","1 4.9 3.0 1.4 0.2\n","2 4.7 3.2 1.3 0.2\n","3 4.6 3.1 1.5 0.2\n","4 5.0 3.6 1.4 0.2\n",".. ... ... ... ...\n","145 6.7 3.0 5.2 2.3\n","146 6.3 2.5 5.0 1.9\n","147 6.5 3.0 5.2 2.0\n","148 6.2 3.4 5.4 2.3\n","149 5.9 3.0 5.1 1.8\n","\n","[150 rows x 4 columns]"],"text/html":["\n","
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sepal-lengthsepal-widthpetal-lengthpetal-width
05.13.51.40.2
14.93.01.40.2
24.73.21.30.2
34.63.11.50.2
45.03.61.40.2
...............
1456.73.05.22.3
1466.32.55.01.9
1476.53.05.22.0
1486.23.45.42.3
1495.93.05.11.8
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150 rows × 4 columns

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\n"," "]},"metadata":{},"execution_count":944}]},{"cell_type":"markdown","source":["### Menentukan Cluster dan Menghitung jarak"],"metadata":{"id":"y5ow9wgwsZNl"}},{"cell_type":"markdown","source":["Pada Kasus ini, cluster yang ditentukan adalah **3**"],"metadata":{"id":"6jfoOZDlsg7-"}},{"cell_type":"code","source":["from sklearn.cluster import KMeans\n","from sklearn import preprocessing\n","from sklearn.metrics import accuracy_score\n","\n","# Number of clusters\n","kmeans = KMeans(n_clusters=3)\n","# Fitting the input data\n","kmeans = kmeans.fit(x)\n","# Getting the cluster labels\n","labels = kmeans.predict(x)\n","# Centroid values\n","centroids = kmeans.cluster_centers_\n","hasil = kmeans.fit_transform(x)\n","hasil"],"metadata":{"id":"mWI2Wc0JF5yP","executionInfo":{"status":"ok","timestamp":1664672350502,"user_tz":-420,"elapsed":20,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"f5fff6ed-1e3d-4eca-d6fd-f04fc3fe54c5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.14694217, 3.41925061, 5.0595416 ],\n"," [0.43816892, 3.39857426, 5.11494335],\n"," [0.41230086, 3.56935666, 5.27935534],\n"," [0.51883716, 3.42240962, 5.15358977],\n"," [0.19796969, 3.46726403, 5.10433388],\n"," [0.68380699, 3.14673162, 4.68148797],\n"," [0.41520116, 3.51650264, 5.21147652],\n"," [0.0599333 , 3.33654987, 5.00252706],\n"," [0.80099438, 3.57233779, 5.32798107],\n"," [0.36659514, 3.3583767 , 5.06790865],\n"," [0.48784424, 3.32449131, 4.89806763],\n"," [0.25138019, 3.31126872, 4.9966845 ],\n"," [0.49192682, 3.46661272, 5.19103612],\n"," [0.90906105, 3.90578362, 5.65173594],\n"," [1.02019214, 3.646649 , 5.10804455],\n"," [1.21309192, 3.49427881, 4.88564095],\n"," [0.66241377, 3.495248 , 5.03090587],\n"," [0.1509702 , 3.38444981, 5.02342022],\n"," [0.82848778, 3.11245944, 4.61792995],\n"," [0.39898872, 3.37738931, 4.97213426],\n"," [0.46172719, 3.07471224, 4.6955761 ],\n"," [0.33762701, 3.31506588, 4.9236821 ],\n"," [0.64435394, 3.93167253, 5.59713396],\n"," [0.37946278, 3.01233762, 4.68193765],\n"," [0.4845534 , 3.06241269, 4.75095704],\n"," [0.44180539, 3.19414543, 4.90772894],\n"," [0.20782685, 3.17967089, 4.84545508],\n"," [0.21815591, 3.30941724, 4.93969029],\n"," [0.2097427 , 3.37648183, 5.01833618],\n"," [0.40198507, 3.31272968, 5.02954567],\n"," [0.40495926, 3.26550651, 4.98608729],\n"," [0.42566654, 3.18083736, 4.79550372],\n"," [0.72442529, 3.53142353, 5.06520776],\n"," [0.9282198 , 3.57102821, 5.04438334],\n"," [0.36659514, 3.3583767 , 5.06790865],\n"," [0.34524194, 3.56904033, 5.25071556],\n"," [0.5287646 , 3.43783276, 5.02368214],\n"," [0.36659514, 3.3583767 , 5.06790865],\n"," [0.75550778, 3.66205264, 5.40750095],\n"," [0.11131936, 3.31092773, 4.9664149 ],\n"," [0.19181241, 3.49764675, 5.14520862],\n"," [1.23935144, 3.60850034, 5.38423754],\n"," [0.66602703, 3.68120561, 5.40847417],\n"," [0.38986151, 3.14278239, 4.78803478],\n"," [0.60761172, 3.00585191, 4.59828494],\n"," [0.47370033, 3.39468045, 5.11844067],\n"," [0.41855943, 3.32788568, 4.92421655],\n"," [0.4673243 , 3.51879523, 5.23766854],\n"," [0.41132955, 3.34104251, 4.92859681],\n"," [0.14139307, 3.40601705, 5.08216833],\n"," [3.97889331, 1.22697525, 1.25489071],\n"," [3.57569462, 0.684141 , 1.44477759],\n"," [4.13182671, 1.17527644, 1.01903626],\n"," [3.00672446, 0.73153652, 2.45978458],\n"," [3.7451291 , 0.63853451, 1.3520017 ],\n"," [3.34604124, 0.26937898, 1.88009327],\n"," [3.74149596, 0.76452634, 1.28902785],\n"," [2.233829 , 1.58388575, 3.37155487],\n"," [3.70928457, 0.75582717, 1.41123804],\n"," [2.79706847, 0.85984838, 2.58955659],\n"," [2.5937602 , 1.53611907, 3.27864111],\n"," [3.16815277, 0.32426175, 1.90055758],\n"," [3.07805003, 0.80841374, 2.38073698],\n"," [3.64323922, 0.39674141, 1.45909603],\n"," [2.50973943, 0.87269542, 2.60303733],\n"," [3.59544045, 0.87306498, 1.50822767],\n"," [3.36487622, 0.41229163, 1.85387593],\n"," [2.9438057 , 0.53579956, 2.25517257],\n"," [3.70189033, 0.6367639 , 1.74778451],\n"," [2.80399572, 0.71254917, 2.49557781],\n"," [3.79431048, 0.7093731 , 1.37094403],\n"," [3.02079327, 0.46349013, 2.06563694],\n"," [3.98757972, 0.69373966, 1.29106776],\n"," [3.60060995, 0.43661144, 1.57547425],\n"," [3.37188256, 0.54593856, 1.70495043],\n"," [3.55977415, 0.74313017, 1.52298639],\n"," [4.00819061, 0.98798453, 1.18965415],\n"," [4.20328348, 1.06739835, 0.84636259],\n"," [3.47148268, 0.21993519, 1.61913335],\n"," [2.42231129, 1.0243726 , 2.77868071],\n"," [2.73312861, 0.86396528, 2.6440625 ],\n"," [2.61755458, 0.97566381, 2.75566654],\n"," [2.82736485, 0.55763082, 2.32254696],\n"," [4.06974102, 0.73395781, 1.22324554],\n"," [3.33538484, 0.57500396, 1.9942056 ],\n"," [3.47050313, 0.68790275, 1.61049622],\n"," [3.87556344, 0.92700552, 1.19803047],\n"," [3.55803204, 0.61459444, 1.81572464],\n"," [2.93107352, 0.50830256, 2.20430516],\n"," [2.9382294 , 0.6291191 , 2.40438484],\n"," [3.23221163, 0.48790256, 2.14635877],\n"," [3.54152397, 0.38266958, 1.52402278],\n"," [2.94020271, 0.49185351, 2.26286106],\n"," [2.27868208, 1.5485635 , 3.33648305],\n"," [3.07720523, 0.3856087 , 2.16211718],\n"," [3.00931753, 0.44284695, 2.11299567],\n"," [3.05790647, 0.3449879 , 2.07973003],\n"," [3.29423618, 0.37241653, 1.76829182],\n"," [1.98584793, 1.66064034, 3.44291999],\n"," [2.98784069, 0.38393196, 2.16527941],\n"," [5.23002792, 2.0445799 , 0.77731871],\n"," [4.13627755, 0.85382472, 1.29757391],\n"," [5.2614059 , 2.05245342, 0.30610139],\n"," [4.63361544, 1.33089245, 0.65293923],\n"," [5.00335807, 1.72813078, 0.38458885],\n"," [6.06026336, 2.87401886, 1.14225684],\n"," [3.49158875, 1.07101875, 2.4108337 ],\n"," [5.59810611, 2.39730707, 0.78573677],\n"," [4.99343489, 1.67668563, 0.65454939],\n"," [5.60613878, 2.54158648, 0.8435596 ],\n"," [4.31086905, 1.17541367, 0.74552218],\n"," [4.46273369, 1.13563278, 0.75289837],\n"," [4.80907392, 1.59322675, 0.25958095],\n"," [4.11232197, 0.88917352, 1.48572618],\n"," [4.34524936, 1.20227628, 1.30303821],\n"," [4.57523682, 1.42273608, 0.68288333],\n"," [4.5953446 , 1.33403966, 0.50991553],\n"," [6.21652572, 3.20105585, 1.47791217],\n"," [6.4578628 , 3.20759942, 1.52971038],\n"," [4.0684631 , 0.82617494, 1.53708992],\n"," [5.07992047, 1.91251832, 0.26952816],\n"," [3.95277017, 0.81891975, 1.5334904 ],\n"," [6.17566126, 2.9794431 , 1.31149299],\n"," [4.05181342, 0.74269596, 1.10668455],\n"," [4.92666134, 1.75847731, 0.27627819],\n"," [5.27802918, 2.14580999, 0.52766931],\n"," [3.91887637, 0.62526165, 1.20765678],\n"," [3.94953061, 0.70228926, 1.16212743],\n"," [4.78292714, 1.4663925 , 0.54629196],\n"," [5.0624097 , 1.93773659, 0.59428255],\n"," [5.50890116, 2.31885342, 0.7312665 ],\n"," [5.99739877, 3.07340053, 1.43802246],\n"," [4.82261257, 1.51444141, 0.5605572 ],\n"," [4.10541009, 0.81536685, 1.05631592],\n"," [4.50652771, 1.23209127, 1.12133058],\n"," [5.75777665, 2.6381171 , 0.95311851],\n"," [4.84041238, 1.72401927, 0.73306362],\n"," [4.55574275, 1.31541133, 0.57903109],\n"," [3.83572575, 0.61011676, 1.29960041],\n"," [4.75659458, 1.60532899, 0.34794609],\n"," [4.97248348, 1.77481954, 0.3893492 ],\n"," [4.59738969, 1.53937059, 0.68403844],\n"," [4.13627755, 0.85382472, 1.29757391],\n"," [5.21259935, 2.00764279, 0.30952112],\n"," [5.09085376, 1.94554509, 0.50939919],\n"," [4.60751473, 1.44957743, 0.61173881],\n"," [4.21459274, 0.89747884, 1.10072376],\n"," [4.40998776, 1.17993324, 0.65334214],\n"," [4.59839015, 1.50889317, 0.83572418],\n"," [4.07622276, 0.83452741, 1.1805499 ]])"]},"metadata":{},"execution_count":945}]},{"cell_type":"markdown","source":["### Mengklasifikasikan Hasil"],"metadata":{"id":"GRl_vo-Asrh2"}},{"cell_type":"code","source":["labels\n","if(labels[1] == 0):\n"," print(labels)\n","elif labels[57] ==2:\n"," # print(labels)\n"," mapping = {0:2, 1:0, 2:1}\n"," a = [mapping[i] for i in labels]\n"," print(a,end='')\n","elif labels[1]==1:\n"," mapping = {0:1, 1:0, 2:2}\n"," a = [mapping[i] for i in labels]\n"," print(a,end='')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xblmWmM_adN6","executionInfo":{"status":"ok","timestamp":1664672350502,"user_tz":-420,"elapsed":11,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"5667c880-2a34-4310-e352-b49f2c5ee6a5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n"," 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n"," 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2 2 2 1 2 2 2 2\n"," 2 2 1 1 2 2 2 2 1 2 1 2 1 2 2 1 1 2 2 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 2\n"," 2 1]\n"]}]},{"cell_type":"markdown","source":["### Menghitung Akurasi"],"metadata":{"id":"MVfy398KtCBB"}},{"cell_type":"markdown","source":["\n","\n","Rumus Menghitung akurasi:\n","> $Akurasi = {{TP + TN}\\over Total Data} * 100 \\% $\n","\n","```\n"," Ket:\n"," TP = Jumlah data yang terklasifikasi True Positive\n"," TN = Jumlah data yang terklasifikasi True Negative\n"," Jumlah Data = Jumlah data keseluruhan\n","``` \n","\n"],"metadata":{"id":"IhWE-hn9tH-p"}},{"cell_type":"markdown","source":["Akurasi yang diperoleh dari hasil ***K-mean Clustering*** dengan data uji acak pada kasus ini sebesar:"],"metadata":{"id":"TcVTBrRBHk-G"}},{"cell_type":"code","source":["# rumus akurasu\n","if(labels[1] == 0):\n"," accuracy = accuracy_score(y, labels)\n","elif labels[55]==2:\n"," accuracy = accuracy_score(y, a)\n","elif labels[1]==1:\n"," accuracy = accuracy_score(y, a)\n","\n","accuracy"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kWx6uAwGF78H","executionInfo":{"status":"ok","timestamp":1664672350503,"user_tz":-420,"elapsed":9,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"a0584ffa-e710-42c4-977d-4c8622a36760"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8933333333333333"]},"metadata":{},"execution_count":948}]}]}