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XML Data Clustering Algorithms Study by Marco Campus is a document available to read on EtoBox.

This study compares three clustering algorithms (K-means, EM, and Tree Clustering) for XML data clustering, highlighting their methodologies, advantages, and limitations. The results indicate that while K-means is efficient and performs well with larger datasets, Tree Clustering provides higher quality clusters. The paper concludes that combining K-means with Tree Clustering could yield the best performance for XML data clustering.

Author

Marco Campus

Language

EN