A Comparative Analysis of Clustering Methods on the 20 Newsgroups Dataset for Analytics
DOI:
https://doi.org/10.32628/CSEIT25112788Keywords:
Large Language Model (LLM), Clustering, Embedding, Analytics, Natural Language Processing (NLP), Dynamic Classification, Unsupervised K-Means ClusteringAbstract
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.
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