Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/117199
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Conference paper |
Title: | A framework for clustering and dynamic maintenance of XML documents |
Author: | Al-Shammari, A. Liu, C. Naseriparsa, M. Vo, B. Anwar, T. Zhou, R. |
Citation: | Lecture Notes in Artificial Intelligence, 2017 / Cong, G., Peng, W., Zhang, W., Li, C., Sun, A. (ed./s), vol.10604 LNAI, pp.399-412 |
Publisher: | Springer |
Issue Date: | 2017 |
Series/Report no.: | Lecture Notes in Artificial Intelligence; 10604 |
ISBN: | 9783319691787 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | International Conference on Advanced Data Mining and Applications (ADMA) (5 Nov 2017 - 6 Nov 2017 : Singapore) |
Editor: | Cong, G. Peng, W. Zhang, W. Li, C. Sun, A. |
Statement of Responsibility: | Ahmed Al-Shammari, Chengfei Liu, Mehdi Naseriparsa, Bao Quoc Vo, Tarique Anwar, and Rui Zhou |
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. |
Keywords: | Clustering; XML documents; Structure and content similarity; dynamic maintenance |
Rights: | © Springer International Publishing AG 2017 |
DOI: | 10.1007/978-3-319-69179-4_28 |
Grant ID: | http://purl.org/au-research/grants/arc/DP170104747 |
Published version: | http://dx.doi.org/10.1007/978-3-319-69179-4_28 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.