A framework for clustering and dynamic maintenance of XML documents
Date
2017
Authors
Al-Shammari, A.
Liu, C.
Naseriparsa, M.
Vo, B.
Anwar, T.
Zhou, R.
Editors
Cong, G.
Peng, W.
Zhang, W.
Li, C.
Sun, A.
Peng, W.
Zhang, W.
Li, C.
Sun, A.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
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
Statement of Responsibility
Ahmed Al-Shammari, Chengfei Liu, Mehdi Naseriparsa, Bao Quoc Vo, Tarique Anwar, and Rui Zhou
Conference Name
International Conference on Advanced Data Mining and Applications (ADMA) (5 Nov 2017 - 6 Nov 2017 : Singapore)
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.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
© Springer International Publishing AG 2017