Python uses Pattern clustering analysis

Preparation work: Before using the Pattern library for clustering analysis, it is necessary to first install Python and the Pattern library. The following are the steps for preparation: 1. Install Python: Python is a universal programming language that can be found on the official website（ https://www.python.org/ ）Download the Python version suitable for your operating system and follow the installation steps. 2. Install the Pattern library: Pattern is a Python library for data mining, machine learning, Natural language processing and other tasks. You can use the following command to install the Pattern library on the command line: pip install pattern Dependent class libraries: Before using the Pattern library for clustering analysis, we also need to install some additional class libraries. These libraries include numpy, scipy, and matplotlib. You can use the following commands to install these class libraries separately: pip install numpy pip install scipy pip install matplotlib Dataset: In this example, we will use a sample dataset for clustering analysis. This dataset is an iris dataset that includes the length and width of the sepals and petals of three different types of iris. Dataset download website: This dataset can be downloaded from the UCI Machine Learning Repository website. Download website: https://archive.ics.uci.edu/ml/datasets/iris Sample data: Each row of this dataset contains four feature values and a category label. The characteristic values include the length of the calyx, the width of the calyx, the length of the petals, and the width of the petals. The category label represents the type of iris to which it belongs. Complete example: The following is a complete example of using the Pattern library for clustering analysis: python from pattern.en import parsetree from pattern.vector import KMeans, count #Read Dataset def read_dataset(file_path): dataset = [] with open(file_path, 'r') as file: for line in file: line = line.strip() if line: dataset.append(line.split(',')) return dataset #Preprocessing and feature extraction of text def preprocess_text(text): sentences = parsetree(text, lemmata=True, encoding='utf-8') return count([sentence.lemmata for sentence in sentences]) #Load Dataset dataset = read_dataset('iris.data') #Pre processing and feature extraction preprocessed_data = [preprocess_text(data[0]) for data in dataset] #Cluster analysis using KMeans KMeans=KMeans (3) # Select 3 cluster clusters here clusters = kmeans.cluster(preprocessed_data, iterations=20, distance='cosine') #Output clustering results for i, cluster in enumerate(clusters): print("Cluster {}: ".format(i + 1)) for j, document in enumerate(cluster): print("Document {}: {}".format(j + 1, dataset[document.original])) print() This example uses the KMeans class from the Pattern library for clustering analysis. Firstly, it reads the Iris dataset, and then preprocesses and extracts features from each data point. Next, it uses the KMeans algorithm to cluster and analyze the data, dividing it into three different clustering clusters. Finally, it outputs the data points contained in each cluster. Note: In this example, the English module (pattern. en) of the Pattern library is used, so it is necessary to pay attention to whether the dataset matches it. If using datasets from other languages, you can choose the corresponding Pattern module for processing.