@inproceedings{3ce5e390e890475ea5dcd33467bfd83d,
title = "A framework for clustering and dynamic maintenance of XML documents",
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, Dynamic maintenance, Structure and content similarity, XML documents",
author = "Ahmed Al-Shammari and Chengfei Liu and Mehdi Naseriparsa and Vo, {Bao Quoc} and Tarique Anwar and Rui Zhou",
year = "2017",
doi = "10.1007/978-3-319-69179-4_28",
language = "English",
isbn = "9783319691787",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "399--412",
editor = "Gao Cong and Wen-Chih Peng and {Zhang }, {Wei Emma} and Chengliang Li and Aixin Sun",
booktitle = "Advanced Data Mining and Applications",
address = "United States",
note = "13th International Conference on Advanced Data Mining and Applications, ADMA 2017 ; Conference date: 05-11-2017 Through 06-11-2017",
}