K-means algorithm is a common unsupervised learning algorithm used to cluster data into different categories. The K-means algorithm is an improved version of the K-means algorithm, aiming to improve the efficiency and accuracy of initial cluster center selection. This article will introduce in detail the principle, code implementation and application of the K-means algorithm in Python.
K-means algorithm is an iterative algorithm. The process of each iteration is: first randomly select K initial clustering centers, Then each data point is assigned to the category of the initial cluster center closest to it, and then the centers of all clusters are recalculated and the cluster centers are updated. Repeat the above process until the convergence conditions are met.
K-means algorithm process:
The K-means algorithm is an improved version of the K-means algorithm, which is mainly optimized in the selection of the initial clustering center. The initial cluster center selection steps of the K-means algorithm are as follows:
Next, we will implement the K-means algorithm through Python.
First, import the necessary libraries:
import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import make_blobs from sklearn.cluster import KMeans
Next, we generate a set of data for clustering:
n_samples = 1500 random_state = 170 X, y = make_blobs(n_samples=n_samples, random_state=random_state)
Then, we train through the KMeans module of sklearn K-means model:
kmeans = KMeans(init="k-means++", n_clusters=3, n_init=10) kmeans.fit(X)
Finally, we visualize the clustering results:
plt.figure(figsize=(12, 12)) h = 0.02 x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) plt.imshow(Z, interpolation="nearest", extent=(xx.min(), xx.max(), yy.min(), yy.max()), cmap=plt.cm.Pastel1, aspect="auto", origin="lower") plt.scatter(X[:, 0], X[:, 1], s=30, c=kmeans.labels_, cmap=plt.cm.Paired) plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], marker="^", s=100, linewidths=3, color='black', zorder=10) plt.title("K-means++ clustering") plt.xlim(x_min, x_max) plt.ylim(y_min, y_max) plt.show()
K-means algorithm Suitable for data clustering problems without label information. Compared with the K-means algorithm, in order to ensure the rationality and uniqueness of the initial clustering center, the K-means algorithm is more suitable for situations where there is a lot of data or the data distribution is relatively scattered.
K-means algorithm can be used in data mining, image processing, natural language processing and other fields. Clustering algorithms can be used to find samples with higher similarity, which is also very useful for the visualization of big data.
In short, the K-means algorithm has good application prospects in data mining, cluster analysis, image recognition, natural language processing and other fields.
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