Efficient Substructure Discovery from Large Semi-Structured Data(Data Mining)
スポンサーリンク
概要
- 論文の詳細を見る
In this paper, we consider a data mining problem for semi-structured data. Modeling semi-structured data as labeled ordered trees, we present an efficient algorithm for discovering frequent substructures from a large collection of semi-structured data. By extending the enumeration technique developed by Bayardo (SIGMOD'98) for discovering long item-sets, our algorithm scales almost linearly in the total size of maximal tree patterns contained in an input collection depending mildly on the size of the longest pattern. We also developed several pruning techniques that significantly speed-up the search. Experiments on Web data show that our algorithm runs efficiently on real-life datasets combined with proposed pruning techniques in the wide range of parameters.
- 社団法人電子情報通信学会の論文
- 2004-12-01
著者
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ABE Kenji
Department of Biochemistry 1, Hamamatsu University School of Medicine
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Sakamoto Hiroshi
Department of Cardiology, The Jikei University School of Medicine
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ARIKAWA SETSUO
Department of Informatics, Kyushu University
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Arikawa Setsuo
Department Of Informatics Kyushu University
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ASAI Tatsuya
Department of Orthopaedics, Osaka University Graduate School of Medicine
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Asai Tatsuya
Department Of Informatics Kyushu University
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Asai Tatsuya
Department Of Applied Physics Chubu University
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Arimura Hiroki
Department Of Informatics Kyushu University
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KAWASOE Shinji
Department of Informatics, Kyushu University
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Kawasoe Shinji
Department Of Informatics Kyushu University
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Abe Kenji
Department Of Biochemistry 1 Hamamatsu University School Of Medicine
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Sakamoto Hiroshi
Department Of Bioscience And Bioinformatics Graduate School Of Computer Science And Systems Engineer
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Sakamoto Hiroshi
Department Of Informatics Kyushu University
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Sakamoto Hiroshi
Deparment Of Biology Faculty Of Science Kobe University
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Abe Kenji
Department Of Informatics Kyushu University
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