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K-Means clustering is an unsupervised machine learning algorithm used to partition data into distinct groups or clusters. It is particularly useful for identifying patterns and structures in unlabeled data. The algorithm works by iteratively assigning data points to clusters based on their distance from the cluster centroids and updating the centroids until convergence.
Implementation of K-Means Clustering in Python
To implement K-Means clustering in Python, we can use the scikit-learn library, which provides a robust implementation of the algorithm. Below is a step-by-step guide to performing K-Means clustering:
Step 1: Import Necessary Libraries
First, we need to import the required libraries:
import numpy as npimport matplotlib.pyplot as pltfrom sklearn.datasets import make_blobsfrom sklearn.cluster import KMeansfrom sklearn.preprocessing import StandardScalerCopied!✕CopyStep 2: Generate Synthetic Data
We can use make_blobs to generate synthetic data for clustering:
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- To perform k-means clustering in Python, we can use the KMeans function from the sklearnmodule. This function uses the following basic syntax: KMeans(init=’random’, n_clusters=8, n_init=10, random_state=None) where: 1. init: Controls the initialization technique. 2. n_clusters: The number of clusters to place observations in. 3. n_init: The number ...
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