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In this study, we introduce the Double Spherical K-Means (DSKM) clustering method for text data. A novel approach for simultaneous clustering of terms and documents. Leveraging the strengths of k-means, double k-means, and spherical k-means, DSKM addresses the challenges of high dimensionality, noise, and sparsity inherent in text analysis. We apply DSKM to the corpus of US presidential inaugural addresses, spanning from George Washington in 1789 to Joe Biden in 2021. Our analysis reveals distinct clusters of words and documents that correspond to significant historical themes and periods, showcasing the method’s ability to facilitate a deeper understanding of the data. Our findings demonstrate DSKM’s efficacy in uncovering underlying patterns in textual data.

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A New Approach for Text Bi-Clustering

  • 会议论文

  • 发表日期：

    2025年

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  • Ilaria Bombelli,
  • Domenica Fioredistella Iezzi,
  • Emiliano Seri

摘要

In this study, we introduce the Double Spherical K-Means (DSKM) clustering method for text data. A novel approach for simultaneous clustering of terms and documents. Leveraging the strengths of k-means, double k-means, and spherical k-means, DSKM addresses the challenges of high dimensionality, noise, and sparsity inherent in text analysis. We apply DSKM to the corpus of US presidential inaugural addresses, spanning from George Washington in 1789 to Joe Biden in 2021. Our analysis reveals distinct clusters of words and documents that correspond to significant historical themes and periods, showcasing the method’s ability to facilitate a deeper understanding of the data. Our findings demonstrate DSKM’s efficacy in uncovering underlying patterns in textual data.