Clustering News Paper Articles Without Using Predefined Categories
In this blog post, I will show you how to cluster news paper articles without using predefined categories by using k-means clustering algorithm and a dataset. K-means clustering is a popular unsupervised machine learning technique that partitions a set of data points into k groups based on their similarity. The goal is to minimize the distance between each data point and the centroid of its assigned cluster.
To apply k-means clustering to news paper articles, we need two things: a dataset of articles and a way to measure the similarity between them. For the dataset, I will use the 20 Newsgroups dataset, which contains about 20,000 articles from 20 different topics. For the similarity measure, I will use the TF-IDF (term frequency-inverse document frequency) vectorizer, which converts each article into a numerical vector that represents how important each word is in the article and in the whole corpus.
The steps of the clustering process are as follows:
1. Import the necessary libraries and load the dataset.
2. Preprocess the articles by removing stopwords, punctuation, numbers, and stemming the words.
3. Vectorize the articles using TF-IDF and normalize the vectors.
4. Choose a value for k and initialize k random centroids.
5. Assign each article to the closest centroid based on the cosine similarity.
6. Update the centroids by taking the mean of the articles in each cluster.
7. Repeat steps 5 and 6 until the centroids converge or a maximum number of iterations is reached.
8. Evaluate the clustering results using some metrics such as silhouette score, homogeneity score, and completeness score.
The results show that k-means clustering can group news paper articles into meaningful clusters without using predefined categories. However, there are some limitations and challenges that need to be addressed, such as:
- Choosing an optimal value for k is not trivial and may require some trial and error or other methods such as elbow method or gap statistic.
- The clustering quality depends on the similarity measure and the vectorization method, which may not capture all the nuances and semantics of natural language.
- The clustering results may not be stable and may vary depending on the initial centroids and the order of the data points.
In conclusion, k-means clustering is a simple and powerful technique that can be used to cluster news paper articles without using predefined categories. However, it also has some drawbacks and limitations that need to be considered and improved. In future work, I will explore other clustering algorithms and methods that can overcome some of these challenges and provide better results.
