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Machine Learning for Beginners in Python: k-Means Clustering

By on Wednesday, May 26, 2021

k-Means Clustering

Preliminaries


/* Load libraries */
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans

Load Iris Flower Dataset


/* Load data */
iris = datasets.load_iris()
X = iris.data

Standardize Features


/* Standarize features */
scaler = StandardScaler()
X_std = scaler.fit_transform(X)

Conduct k-Means Clustering


/* Create k-mean object */
clt = KMeans(n_clusters=3, random_state=0, n_jobs=-1)

/* Train model */
model = clt.fit(X_std)

Show Each Observation’s Cluster Membership


/* View predict class */
model.labels_
array([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, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2,
       2, 0, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 2,
       0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0,
       2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2], dtype=int32)

Create New Observation


/* Create new observation */
new_observation = [[0.8, 0.8, 0.8, 0.8]]

Predict Observation’s Cluster


/* Predict observation's cluster */
model.predict(new_observation)
array([0], dtype=int32)

View Centers Of Each Cluster


/* View cluster centers */
model.cluster_centers_
array([[ 1.13597027,  0.09659843,  0.996271  ,  1.01717187],
       [-1.01457897,  0.84230679, -1.30487835, -1.25512862],
       [-0.05021989, -0.88029181,  0.34753171,  0.28206327]])

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

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  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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