Presentation 2003/10/1
An Efficient Algorithm for Mining Frequent Unordered Trees from Semi-structured Data
Tatsuya ASAI, Hiroki ARIMURA, Takeaki UNO, Shinichi NAKANO,
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Abstract(in English) In this paper, we study a data mining problem of discovering frequent substructures in a large collection of semi-structured data, where both of the patterns and the data are modeled by labeled unordered trees. The keys of the algorithm are efficient enumerating all unordered trees and incrementally computation of the occurrences based on a powerful design technique known as the reverse search. We present an efficient algorithm called U NOT that computes all labeled unordered trees appearing in a collection of data trees with frequency above a user-specified threshold. We prove that the algorithm enumerates each frequent pattern T in O(kb^2m) per pattern, where k is the size of T, b is the branching factor of the data tree, and m is the total number of occurrences of T in the data trees.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) semi-structured data mining / graph mining / frequent pattern discovery / labeled unordered trees / reverse search
Paper # DE2003-108,DC2003-21
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Committee DE
Conference Date 2003/10/1(1days)
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Registration To Data Engineering (DE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) An Efficient Algorithm for Mining Frequent Unordered Trees from Semi-structured Data
Sub Title (in English)
Keyword(1) semi-structured data mining
Keyword(2) graph mining
Keyword(3) frequent pattern discovery
Keyword(4) labeled unordered trees
Keyword(5) reverse search
1st Author's Name Tatsuya ASAI
1st Author's Affiliation Department of Informatics, Kyushu University()
2nd Author's Name Hiroki ARIMURA
2nd Author's Affiliation Department of Informatics, Kyushu University
3rd Author's Name Takeaki UNO
3rd Author's Affiliation Natinal Institute of Informatics
4th Author's Name Shinichi NAKANO
4th Author's Affiliation Faculty of Engineering, Gunma University
Date 2003/10/1
Paper # DE2003-108,DC2003-21
Volume (vol) vol.103
Number (no) 355
Page pp.pp.-
#Pages 6
Date of Issue