Title: Efficient Substructure Discovery from Large Semi-structured Data
Abstract: Previous chapter Next chapter Full AccessProceedings Proceedings of the 2002 SIAM International Conference on Data Mining (SDM)Efficient Substructure Discovery from Large Semi-structured DataTatsuya Asai, Kenji Abe, Shinji Kawasoe, Hiroki Arimura, Hiroshi Sakamoto, and Setsuo ArikawaTatsuya Asai, Kenji Abe, Shinji Kawasoe, Hiroki Arimura, Hiroshi Sakamoto, and Setsuo Arikawapp.158 - 174Chapter DOI:https://doi.org/10.1137/1.9781611972726.10PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract 1 Introduction By rapid progress of network and storage technologies, a huge amount of electronic data such as Web pages and XML data [23] has been available on intra and internet. These electronic data are heterogeneous collection of ill-structured data that have no rigid structures, and often called semi-structured data [1]. Hence, there have been increasing demands for automatic methods for extracting useful information, particularly, for discovering rules or patterns from large collections of semi-structured data, namely, semi-structured data mining [6, 11, 18, 19, 21, 25]. Previous chapter Next chapter RelatedDetails Published:2002ISBN:978-0-89871-517-0eISBN:978-1-61197-272-6 https://doi.org/10.1137/1.9781611972726Book Series Name:ProceedingsBook Code:PR108Book Pages:xii + 600
Publication Year: 2002
Publication Date: 2002-04-11
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 348
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