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A framework for clustering and dynamic maintenance of XML documents

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dc.contributor.authorAl-Shammari, A.
dc.contributor.authorLiu, C.
dc.contributor.authorNaseriparsa, M.
dc.contributor.authorVo, B.
dc.contributor.authorAnwar, T.
dc.contributor.authorZhou, R.
dc.contributor.conferenceInternational Conference on Advanced Data Mining and Applications (ADMA) (5 Nov 2017 - 6 Nov 2017 : Singapore)
dc.contributor.editorCong, G.
dc.contributor.editorPeng, W.
dc.contributor.editorZhang, W.
dc.contributor.editorLi, C.
dc.contributor.editorSun, A.
dc.date.issued2017
dc.description.abstractWeb 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.
dc.description.statementofresponsibilityAhmed Al-Shammari, Chengfei Liu, Mehdi Naseriparsa, Bao Quoc Vo, Tarique Anwar, and Rui Zhou
dc.identifier.citationLecture Notes in Artificial Intelligence, 2017 / Cong, G., Peng, W., Zhang, W., Li, C., Sun, A. (ed./s), vol.10604 LNAI, pp.399-412
dc.identifier.doi10.1007/978-3-319-69179-4_28
dc.identifier.isbn9783319691787
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.orcidZhou, R. [0000-0001-6807-4362]
dc.identifier.urihttp://hdl.handle.net/2440/117199
dc.language.isoen
dc.publisherSpringer
dc.relation.granthttp://purl.org/au-research/grants/arc/DP170104747
dc.relation.ispartofseriesLecture Notes in Artificial Intelligence; 10604
dc.rights© Springer International Publishing AG 2017
dc.source.urihttps://doi.org/10.1007/978-3-319-69179-4_28
dc.subjectClustering; XML documents; Structure and content similarity; dynamic maintenance
dc.titleA framework for clustering and dynamic maintenance of XML documents
dc.typeConference paper
pubs.publication-statusPublished

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