How to use support vector clustering technique in Python?

Support Vector Clustering (SVC) is an unsupervised learning algorithm based on Support Vector Machine (SVM), which can achieve clustering in unlabeled data sets. Python is a popular programming language with a rich set of machine learning libraries and toolkits. This article will introduce how to use support vector clustering technology in Python.

1. The principle of support vector clustering

SVC is based on a set of support vectors and divides the data set into different clusters by finding the smallest hypersphere. Support vector machine is a supervised learning algorithm that uses a kernel function at the bottom to nonlinearly transform the decision boundary. Support vector clustering applies the properties of support vector machines to clustering without requiring label information. By optimizing the spatial manifold or kernel density, the radius of the hypersphere can be minimized and the training samples can be clicked on the spatial manifold. Perform clustering.

2. Use Python for support vector clustering

In Python, you can use the scikit-learn library to implement SVC. The following are the basic steps to implement support vector clustering:

1. Import the necessary libraries and data sets

import numpy as np
import pandas as pd
from sklearn.cluster import SpectralClustering
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt

#使用make_blobs生成随机数据集
X, y = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)
plt.scatter(X[:, 0], X[:, 1])
plt.show()

2. Data standardization

#标准化数据
scaler = StandardScaler()
X = scaler.fit_transform(X)
plt.scatter(X[:, 0], X[:, 1])
plt.show()

3. Use support vector clustering Class algorithm for clustering

#使用支持向量聚类
spectral = SpectralClustering(n_clusters=4, gamma=1)
spectral.fit(X)
y_pred = spectral.labels_.astype(np.int)

#可视化聚类结果
plt.scatter(X[:, 0], X[:, 1], c=y_pred)
plt.show()

3. Application of support vector clustering

Support vector clustering can be used for clustering in unlabeled data sets. Support vector clustering has been applied in fields such as text clustering, image clustering, and phone record clustering. Support vector clustering is most commonly used for image segmentation because many images are high-dimensional sparse features and different objects and shapes in the image can be discovered by using the SVC algorithm.

In the example introduced in this article, clustering is implemented by generating a random data set and using the SpectralClustering algorithm. It can be seen that the distribution relationship of the four cluster points is obvious.

4. Summary

This article introduces how to use the support vector clustering algorithm in Python, including the implementation process of data set import, data standardization and support vector clustering. Support vector clustering can be used for clustering in unlabeled data sets and has applications in fields such as text clustering, image clustering, and phone record clustering. By practicing support vector clustering technology, you can better understand its principles and application scenarios, and help you further learn and use machine learning algorithms.

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