scikit-learn: clustering text documents using DBSCAN

Scikit-learn: Clustering Text Documents Using DBSCAN

Introduction

Clustering is a fundamental task in machine learning that involves grouping similar data points together. Text clustering, specifically, aims to group text documents into clusters based on their semantic similarity. DBSCAN, standing for Density-Based Spatial Clustering of Applications with Noise, is a powerful clustering algorithm well-suited for text data due to its ability to handle noise and identify clusters of varying shapes and sizes. This article will explore the application of DBSCAN for clustering text documents using scikit-learn, a popular Python machine learning library.

1. Data Preparation

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer

# Sample text data
documents = [
    "This is a sample document about data science.",
    "Machine learning algorithms are fascinating.",
    "Natural language processing is essential for text analysis.",
    "Data mining techniques are used for discovering patterns.",
    "This document is about data visualization.",
    "Another document related to machine learning.",
    "This document discusses data preprocessing.",
    "Data science is a growing field.",
    "Text analysis is crucial for understanding data.",
    "This document covers deep learning models."
]

# Create a pandas DataFrame
df = pd.DataFrame({"text": documents})

# Initialize TfidfVectorizer
vectorizer = TfidfVectorizer()

# Fit and transform the text data
tfidf_matrix = vectorizer.fit_transform(df["text"])

This code snippet first defines a list of sample text documents and creates a pandas DataFrame to store them. Then, a `TfidfVectorizer` is initialized to convert the text into a numerical representation using the TF-IDF (Term Frequency-Inverse Document Frequency) technique. This technique weighs words based on their importance within each document and across the entire corpus, providing a meaningful representation for clustering.

2. DBSCAN Implementation

from sklearn.cluster import DBSCAN

# Initialize DBSCAN with desired parameters
dbscan = DBSCAN(eps=0.5, min_samples=2)

# Fit DBSCAN to the TF-IDF matrix
clusters = dbscan.fit_predict(tfidf_matrix)

# Add cluster labels to the DataFrame
df["cluster"] = clusters

Here, we instantiate a `DBSCAN` object with specific parameters:

* `eps`: The maximum distance between two samples for them to be considered neighbors.
* `min_samples`: The minimum number of samples required to form a core point, which is a point that has at least `min_samples` neighbors within the `eps` distance.

DBSCAN then fits the TF-IDF matrix to identify clusters based on the defined parameters. The resulting cluster labels are added as a new column (“cluster”) to the DataFrame.

3. Cluster Analysis

print(df)

This code displays the DataFrame with the assigned cluster labels, allowing us to analyze the clusters and identify the grouping of similar text documents.

4. Visualization

import matplotlib.pyplot as plt

# Visualize the clusters using principal component analysis (PCA)
from sklearn.decomposition import PCA

# Reduce dimensionality using PCA
pca = PCA(n_components=2)
reduced_tfidf = pca.fit_transform(tfidf_matrix)

# Plot the clusters
plt.scatter(reduced_tfidf[:, 0], reduced_tfidf[:, 1], c=clusters, cmap="viridis")
plt.xlabel("Principal Component 1")
plt.ylabel("Principal Component 2")
plt.title("DBSCAN Clustering of Text Documents")
plt.show()

To visualize the clusters, we employ dimensionality reduction using Principal Component Analysis (PCA). PCA reduces the high-dimensional TF-IDF matrix to two dimensions, making it suitable for visualization. The code plots the data points with different colors representing each cluster, offering a visual representation of the clustering results.

5. Conclusion

DBSCAN provides an effective way to cluster text documents based on their semantic similarity. By adjusting the parameters, you can fine-tune the clustering behavior to meet specific requirements. Remember to carefully choose the parameters and evaluate the clustering results to ensure that the clusters are meaningful and representative of the underlying data structure.