What is Clustering In Machine Learning? | Types of Machine Learning Clustering

Dataset 'admission.csv'

1. K-means Clustering

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import pandas as pd
my_data=pd.read_csv('admission.csv')
X=my_data[['NORMALIZED_GPA', 'NORMALIZED_SAT']]

random_state = 17
cluster = KMeans(n_clusters=2, random_state=random_state).fit(X)
y_pred=cluster.predict(X)

fig, axs = plt.subplots(1,2)
axs[0].set_aspect('equal')
axs[0].scatter( X["NORMALIZED_GPA"], X["NORMALIZED_SAT"], c=y_pred, s=30, cmap=plt.cm.Paired)
axs[1].set_aspect('equal')
axs[1].scatter( X["NORMALIZED_GPA"], X["NORMALIZED_SAT"], c=my_data['ACCEPT_NUM'].tolist(), s=30, cmap=plt.cm.Paired)

Line 1-6 is import and data block

Line 8-10 is the clustering part.

In line 10, y_pred is the clustering results, for 2 clusters, it is either 0 or 1 for each point. Line 12-16 is for plotting, you can only plot two-dimensional X data.

The left plot is the clustering results. The right plot is the true “Accept” value.

Output:

2. DBSCAN

import pandas as pd
from sklearn.feature_extraction import image
from sklearn.cluster import DBSCAN
import numpy as np
import matplotlib.pyplot as plt

my_data=pd.read_csv('admission.csv')
X=my_data[['NORMALIZED_GPA', 'NORMALIZED_SAT']]

cluster = DBSCAN(eps=0.2)
cluster.fit(X)
Pred=cluster.labels_.astype(np.int)

fig, axs = plt.subplots(1,2)
axs[0].set_aspect('equal')
axs[0].scatter( X["NORMALIZED_GPA"], X["NORMALIZED_SAT"], c=Pred, s=30, cmap=plt.cm.Paired)
axs[1].set_aspect('equal')
axs[1].scatter( X["NORMALIZED_GPA"], X["NORMALIZED_SAT"], c=my_data['ACCEPT_NUM'].tolist(), s=30, cmap=plt.cm.Paired)

3. Dendrogram

import pandas as pd
from scipy.cluster.hierarchy import dendrogram, linkage

my_data=pd.read_csv('customer.csv')


data=my_data[['Age', 'IncomeNum','GenderNum']]
Z = linkage(data)

#dendrogram(Z)  
dendrogram(Z,labels =my_data['ID'].tolist())

4. GMM(GaussianMixture)

import pandas as pd
from sklearn.feature_extraction import image
from sklearn import mixture
import numpy as np
import matplotlib.pyplot as plt

my_data=pd.read_csv('admission.csv')
X=my_data[['NORMALIZED_GPA', 'NORMALIZED_SAT']]

cluster = mixture.GaussianMixture(n_components=2, covariance_type='full')
cluster.fit(X)
Pred=cluster.predict(X)

fig, axs = plt.subplots(1,2)
axs[0].set_aspect('equal')
axs[0].scatter( X["NORMALIZED_GPA"], X["NORMALIZED_SAT"], c=Pred, s=30, cmap=plt.cm.Paired)
axs[1].set_aspect('equal')
axs[1].scatter( X["NORMALIZED_GPA"], X["NORMALIZED_SAT"], c=my_data['ACCEPT_NUM'].tolist(), s=30, cmap=plt.cm.Paired)

5. AgglomerativeClustering

import pandas as pd
from sklearn.feature_extraction import image
from sklearn.cluster import AgglomerativeClustering
import numpy as np
import matplotlib.pyplot as plt

my_data=pd.read_csv('admission.csv')
X=my_data[['NORMALIZED_GPA', 'NORMALIZED_SAT']]

cluster =AgglomerativeClustering(linkage="average", affinity="cityblock",n_clusters=2)
cluster.fit(X)
Pred=cluster.labels_.astype(np.int)

fig, axs = plt.subplots(1,2)
axs[0].set_aspect('equal')
axs[0].scatter( X["NORMALIZED_GPA"], X["NORMALIZED_SAT"], c=Pred, s=30, cmap=plt.cm.Paired)
axs[1].set_aspect('equal')
axs[1].scatter( X["NORMALIZED_GPA"], X["NORMALIZED_SAT"], c=my_data['ACCEPT_NUM'].tolist(), s=30, cmap=plt.cm.Paired)

6. SpectralClustering

import pandas as pd
from sklearn.feature_extraction import image
from sklearn.cluster import SpectralClustering
import numpy as np
import matplotlib.pyplot as plt

my_data=pd.read_csv('admission.csv')
X=my_data[['NORMALIZED_GPA', 'NORMALIZED_SAT']]

spectral =SpectralClustering(n_clusters=2, eigen_solver='arpack',affinity="nearest_neighbors")
spectral.fit(X)
Pred=spectral.labels_.astype(np.int)

fig, axs = plt.subplots(1,2)
axs[0].set_aspect('equal')
axs[0].scatter( X["NORMALIZED_GPA"], X["NORMALIZED_SAT"], c=Pred, s=30, cmap=plt.cm.Paired)
axs[1].set_aspect('equal')
axs[1].scatter( X["NORMALIZED_GPA"], X["NORMALIZED_SAT"], c=my_data['ACCEPT_NUM'].tolist(), s=30, cmap=plt.cm.Paired)

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