Online algorithms for mining semi-structured data stream - Collections

＜conference paper＞
Online algorithms for mining semi-structured data stream

Creator	Creator Name Asai, Tatsuya 浅井, 達哉アサイ, タツヤ Affiliation Affiliation Name Department of Informatics, Kyushu University 九州大学大学院システム情報科学研究院情報理学部門
	Creator Name Arimura, Hiroki 有村, 博紀アリムラ, ヒロキ Affiliation Affiliation Name PRESTO, JST \| Department of Informatics, Kyushu University 独立行政法人科学技術振興機構 \| 九州大学大学院システム情報科学研究院情報理学部門
	Author PID L003738 Creator Name Abe, Kenji 安部, 賢治アベ, ケンジ Affiliation Affiliation Name Department of Informatics, Kyushu University 九州大学大学院システム情報科学研究院情報理学部門
	Author PID L002461 Creator Name Kawasoe, Shinji 川副, 真治カワソエ, シンジ Affiliation Affiliation Name Department of Informatics, Kyushu University 九州大学大学院システム情報科学研究院情報理学部門
	Author PID K000164 Creator Name Arikawa, Setsuo 有川, 節夫アリカワ, セツオ Affiliation Affiliation Name Department of Informatics, Kyushu University 九州大学大学院システム情報科学研究院情報理学部門
Language	English
Publisher	IEEE
Date	2002
Source Title	Proceedings of 2002 IEEE International Conference on Data Mining
First Page	27
Last Page	34
Publication Type	Version of Record
Access Rights	open access
Rights	© 2002 IEEE
Related DOI
Related DOI	Proceedings of 2002 IEEE International Conference on Data Mining \|\| p27-34
Related URI	Proceedings of 2002 IEEE International Conference on Data Mining \|\| p27-34
Related URI	isIdenticalTo http://doi.ieeecomputersociety.org/10.1109/ICDM.2002.1183882
Related HDL
Relation	Proceedings of 2002 IEEE International Conference on Data Mining \|\| p27-34
Abstract	In this paper, we study an online data mining problem from streams of semi-structured data such as XML data. Modeling semi-structured data and patterns as labeled ordered trees, we present an online a...lgorithm StreamT that receives fragments of an unseen possibly infinite semi-structured data in the document order through a data stream, and can return the current set of frequent patterns immediately on request at any time. A crucial part of our algorithm is the incremental maintenance of the occurrences of possibly frequent patterns using a tree sweeping technique. We give modifications of the algorithm to other online mining model. We present theoretical and empirical analyses to evaluate the performance of the algorithm.show more

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Record ID	6228
Peer-Reviewed	Unrefereed
Related URI	http://doi.ieeecomputersociety.org/10.1109/ICDM.2002.1183882
Subject Terms	data mining
	data structures
	hypermedia markup languages
	pattern recognition
DOI	10.1109/ICDM.2002.1183882
Notes	Second IEEE International Conference on Data Mining (ICDM'02), 9-12 Dec. 2002
Type	会議発表論文
Created Date	2009.04.22
Modified Date	2020.12.09

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＜conference paper＞ Online algorithms for mining semi-structured data stream

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＜conference paper＞
Online algorithms for mining semi-structured data stream