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A Comparative Analysis of Clustering Methods on the 20 Newsgroups Dataset for Analytics

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  • Yanas Rajindran
  • Hanza Parayil Salim

Abstract

This paper presents a comparative analysis of two different approaches for clustering textual data from the 20 Newsgroups dataset. The first approach leverages a Large Language Model (LLM) to classify each text into predefined categories using zero-shot classification. The second approach applies to the traditional K-Means clustering algorithm on text embeddings. We evaluate both methods by comparing their predicted clusters against true labels for assessment. For K-Means, we also explore a semi-supervised variant with centroid initialization based on true labels.

Suggested Citation

  • Yanas Rajindran & Hanza Parayil Salim, 2025. "A Comparative Analysis of Clustering Methods on the 20 Newsgroups Dataset for Analytics," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(2), pages 3075-3078, March.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i2:id:1354
    DOI: 10.32628/CSEIT25112788
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112788
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