Clustering Algorithms for Queries: A Comparative Analysis of ‎Farmer Call Center Data

  • Authors

    • C. Kiruthiga Research Scholar, Vels Institute of Science Technology & Advanced Studies (VISTAS), Assistant Professor, PG Department of Information ‎Technology and BCA, Dwaraka Doss Goverdhan Doss Vaishnav College, Affiliated to the University of Madras, Chennai. Chennai, India
    • Dr. K. Dharmarajan Professor, Department of Information Technology, Vels Institute of Science Technology & amp; Advanced Studies (VISTAS), Chennai, India
    https://doi.org/10.14419/7cvtfq26

    Received date: July 15, 2025

    Accepted date: July 24, 2025

    Published date: November 1, 2025

  • Agglomerative Clustering; Calinski-Harabasz Index; DBSCAN; HDBSCAN; GloVe; K-meansSBERT; Silhouette Score; TF-IDF; Word2Vec

  • Abstract

    Extracting insights from queries and feedback helps identify trends, enhance products and services, personalize customer interactions, and ‎craft effective marketing strategies. Data clustering, a powerful method, organizes unstructured data and refines queries by offering ‎suggestions based on similar or related inputs, ultimately enhancing the search experience. This study compares the performance of several ‎clustering algorithms, including Agglomerative Clustering, K-Means (KM), Hierarchical Density-Based Spatial Clustering of Applications ‎with Noise (HDBSCAN), and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), as well as various embeddings, ‎such as Term Frequency-Inverse Document Frequency (TF-IDF)—Sentence-Bidirectional Encoder Representations from Transformers ‎‎(SBERT), Word2Vec, and GloVe. The Calinski-Harabasz Index, Davies-Bouldin Index, and Silhouette Score are used to measure the ‎effectiveness of these algorithms. Results indicated that HDBSCAN outperformed other clustering algorithms within the farmer helpline ‎dataset. The conclusions were derived from the medium-level performance of clustering algorithms. The findings showed that HDBSCAN, ‎combined with different embeddings, achieved a Silhouette Score of 0.85, a Davies-Bouldin Index of 0.66, and a Calinski-Harabasz Index ‎of 4,239.9‎.

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  • How to Cite

    Kiruthiga, C., & Dharmarajan, D. K. . (2025). Clustering Algorithms for Queries: A Comparative Analysis of ‎Farmer Call Center Data. International Journal of Basic and Applied Sciences, 14(SI-1), 529-537. https://doi.org/10.14419/7cvtfq26