Cluster analysis techniques in Python

With the development of big data technology, cluster analysis, as an important data analysis method, has attracted more and more attention. In the Python language, there are also many powerful cluster analysis libraries and tools, such as scikit-learn, pandas, etc. Today we will introduce cluster analysis techniques in Python.

1. What is cluster analysis?

Cluster analysis is an unsupervised learning method for classifying data. It divides data points into several groups by analyzing the similarities in the data set, so that the differences between data points within the groups are as much as possible Small, the difference between data points between groups is as large as possible. Cluster analysis can be applied to various fields, such as biology, sociology, finance, etc.

2. Cluster analysis library in Python

In Python, there are many powerful cluster analysis libraries and tools, such as scikit-learn, pandas, etc. Below we will introduce two very commonly used cluster analysis libraries:

1. scikit-learn

scikit-learn is one of the most popular machine learning libraries in Python. It has many classic machine learning algorithms built-in, including cluster analysis. In scikit-learn, clustering algorithms such as KMeans and DBSCAN can be used.

KMeans algorithm is a commonly used clustering algorithm, which divides the data set into K clusters. The basic idea of ​​the KMeans algorithm is: first randomly select K center points, then assign the data points to the clusters where the nearest center point is located, then recalculate the center point of each cluster, and repeat this process until the center point is located. The point no longer changes or reaches a predetermined number of iterations.

The DBSCAN algorithm is a density-based clustering algorithm. Its idea is to use data points with a density higher than a certain threshold as cluster centers and other points as noise points. The advantage of the DBSCAN algorithm is that it can adaptively find the cluster center and is not sensitive to noise points.

2. pandas

pandas is a commonly used data analysis library in Python. It provides some aggregate functions, such as groupby, pivot_table, etc., which can be used for aggregation and statistics of data sets. analyze. In cluster analysis, you can use the groupby function of pandas to cluster the data set according to the specified columns and calculate the center point of the cluster.

3. Application of cluster analysis

Cluster analysis can be applied to various fields, such as biology, sociology, finance, etc. Below we will take demographic data as an example to briefly introduce the application of cluster analysis.

We use the pandas library to read a demographic data set, which contains information such as per capita income, per capita GDP, and population density in each region. First, we used the KMeans algorithm of the scikit-learn library to perform cluster analysis on the data set and divided the data into 3 clusters. The code is as follows:

from sklearn.cluster import KMeans
import pandas as pd

data = pd.read_csv('data.csv')
x = data[['income','gdp','density']]
kmeans = KMeans(n_clusters=3)
kmeans.fit(x)
labels_1 = kmeans.labels_

Next, we use the DBSCAN algorithm to perform cluster analysis on the data set, setting the radius to 1 and the minimum number of samples to 5. The code is as follows:

from sklearn.cluster import DBSCAN
dbscan = DBSCAN(eps=1, min_samples=5)
dbscan.fit(x)
labels_2 = dbscan.labels_

Finally, we use the groupby function of the pandas library to calculate the mean of each grouping based on "region". The code is as follows:

result = data.groupby('region')[['income','gdp','density']].mean()

4. Summary

Cluster analysis is an important data analysis method. There are also many powerful cluster analysis libraries and tools available in Python, such as scikit -learn, pandas, etc. In practical applications, different clustering algorithms and methods can be selected according to specific data scenarios to perform cluster analysis and data mining.

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