Python uses Scikit born Hierarchical clustering

Preparation work: Before using Scikit ear for Hierarchical clustering, we need to set up the Python environment and install the necessary libraries. 1. Install Python: You can download it from the official Python website（ https://www.python.org ）Download and install the appropriate Python version for your operating system. 2. Install the Scikit learn library: In the Python environment, you can use the pip command to install the Scikit learn library. Open a terminal or command prompt and enter the following command: bash pip install scikit-learn 3. Install other dependent libraries: When using the Hierarchical clustering algorithm, we also need to install some other libraries, such as NumPy and Matplotlib. Execute the following command to install: bash pip install numpy matplotlib Dependent class libraries: In the Hierarchical clustering task, we will use the sklearn.cluster.AgglomerativeClustering class in the Scikit-learn library to perform Hierarchical clustering. Dataset introduction and download website: For this example, let's use the Iris dataset from the UCI machine learning library. This dataset contains 150 samples, divided into 3 categories, each with 50 instances. The dataset can be downloaded from this website: https://archive.ics.uci.edu/ml/datasets/iris Sample data: The Iris dataset contains four features: calyx length, calyx width, petal length, and petal width. Each sample has a corresponding category label, which is the variety of iris. Complete sample code: The following is a complete Python code example of Hierarchical clustering using iris dataset: python import numpy as np from sklearn.cluster import AgglomerativeClustering from sklearn.datasets import load_iris #Load Iris Dataset iris = load_iris() X = iris.data y = iris.target #Execute Hierarchical clustering clustering = AgglomerativeClustering(n_clusters=3).fit(X) #Output clustering labels for each sample Print ("Cluster label of sample:") print(clustering.labels_) This code example loads the Iris dataset and divides it into 3 clusters. Then, Hierarchical clustering is performed by calling the fit method of the AgglomerativeClustering class. Finally, we print out the clustering labels for each sample. Summary: This example implements a simple Hierarchical clustering using the AgglomerativeClustering class of the Scikit-learn library. In practical applications, we can adjust the parameters and data sets according to the needs to carry out more complex Hierarchical clustering tasks.