Abstract
Web data clustering has been widely studied in the data mining communities. However, dynamic maintenance of the web data clusters is still a challenging task. In this paper, we propose a novel framework called XClusterMaint which serves for both clustering and maintenance of the XML documents. For clustering, we take both structure and content into account and propose an efficient solution for grouping the documents based on the combination of structure and content similarity. For maintenance, we propose an incremental approach for maintaining the existing clusters dynamically when we receive new incoming XML documents. Since the dynamic maintenance of the clusters is computationally expensive, we also propose an improved approach which uses a lazy maintenance scheme to improve the performance of the clusters maintenance. The experimental results on real datasets verify the efficiency of the proposed clustering and maintenance model.
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Notes
- 1.
For simplicity, in this paper, we set \(m=\frac{n}{2}\).
- 2.
\(r^{2}_c\) is usually a fraction of \(r^{1}_c\), i.e. \(r^{2}_c= \lambda r^{1}_c, \lambda \in (0,1)\). In the paper, we find \(\lambda \) = 0.8 is fairly good.
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Acknowledgements
This work was partially supported by the ARC Discovery Project under Grant No. DP170104747 and the Iraqi Ministry of Higher Education and Scientific Research.
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Al-Shammari, A., Liu, C., Naseriparsa, M., Vo, B.Q., Anwar, T., Zhou, R. (2017). A Framework for Clustering and Dynamic Maintenance of XML Documents. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_28
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