IMPROVING XML DOCUMENT CLASSIFICATION THROUGH COLLABORATIVE CLUSTERING - Camilla Marino (Academic Reseach Library for International Journal of Computer Science & Information System, 2024)
This study presents a novel approach to improving XML document classification through collaborative clustering techniques. XML (eXtensible Markup Language) documents are widely used for data representation and interchange; however, their hierarchical and semi-structured nature poses significant challenges for effective classification and retrieval. Traditional classification methods often fall short in handling the complexity and variability of XML data. This research introduces a collaborative clustering framework that leverages the relationships among documents to enhance classification accuracy. By employing clustering algorithms that incorporate user feedback and document similarity measures, the proposed method aims to group similar XML documents together while facilitating easier identification and classification of relevant data. The effectiveness of the collaborative clustering approach is evaluated through a series of experiments on benchmark XML datasets, demonstrating significant improvements in classification performance compared to traditional methods. The findings indicate that collaborative clustering can significantly enhance the organization and retrieval of XML documents, making it a valuable tool for information management in various applications.