article

Efficiently Mining Frequent Embedded Unordered Trees

Author:

Mohammed J. ZakiAuthors Info & Claims

Fundamenta Informaticae, Volume 66, Issue 1-2

Pages 33 - 52

Published: 01 November 2004 Publication History

Abstract

Mining frequent trees is very useful in domains like bioinformatics, web mining, mining semi-structured data, and so on. In this paper we introduce SLEUTH, an efficient algorithm for mining frequent, unordered, embedded subtrees in a database of labeled trees. The key contributions of our work are as follows: We give the first algorithm that enumerates all embedded, unordered trees. We propose a new equivalence class extension scheme to generate all candidate trees. We extend the notion of scope-list joins to compute frequency of unordered trees. We conduct performance evaluation on several synthetic and real datasets to show that SLEUTH is an efficient algorithm, which has performance comparable to TreeMiner, that mines only ordered trees.

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Cited By

Yan DQu WGuo GWang XZhou Y(2021)PrefixFPM: a parallel framework for general-purpose mining of frequent and closed patternsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-021-00687-031:2(253-286)Online publication date: 9-Aug-2021
https://dl.acm.org/doi/10.1007/s00778-021-00687-0
Zhao WWu X(2018)A MapReduce-Based Approach for Mining Embedded Patterns from Large Tree DataWeb and Big Data10.1007/978-3-319-96893-3_34(455-462)Online publication date: 23-Jul-2018
https://dl.acm.org/doi/10.1007/978-3-319-96893-3_34
Wu XTheodoratos D(2018)Efficient Discovery of Embedded Patterns from Large Attributed TreesDatabase Systems for Advanced Applications10.1007/978-3-319-91458-9_34(558-576)Online publication date: 21-May-2018
https://dl.acm.org/doi/10.1007/978-3-319-91458-9_34
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Index Terms

Efficiently Mining Frequent Embedded Unordered Trees
1. Information systems
  1. Information systems applications
    1. Data mining
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Trees

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Fundamenta Informaticae

Fundamenta Informaticae Volume 66, Issue 1-2

Advances in Mining Graphs, Trees and Sequences

November 2004

201 pages

ISSN:0169-2968

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IOS Press

Netherlands

Publication History

Published: 01 November 2004

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Cited By

Yan DQu WGuo GWang XZhou Y(2021)PrefixFPM: a parallel framework for general-purpose mining of frequent and closed patternsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-021-00687-031:2(253-286)Online publication date: 9-Aug-2021
https://dl.acm.org/doi/10.1007/s00778-021-00687-0
Zhao WWu X(2018)A MapReduce-Based Approach for Mining Embedded Patterns from Large Tree DataWeb and Big Data10.1007/978-3-319-96893-3_34(455-462)Online publication date: 23-Jul-2018
https://dl.acm.org/doi/10.1007/978-3-319-96893-3_34
Wu XTheodoratos D(2018)Efficient Discovery of Embedded Patterns from Large Attributed TreesDatabase Systems for Advanced Applications10.1007/978-3-319-91458-9_34(558-576)Online publication date: 21-May-2018
https://dl.acm.org/doi/10.1007/978-3-319-91458-9_34
Benner-Wickner MBrückmann TGruhn VBook MLindstaedt SLey TSack H(2015)Process mining for knowledge-intensive business processesProceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business10.1145/2809563.2809580(1-8)Online publication date: 21-Oct-2015
https://dl.acm.org/doi/10.1145/2809563.2809580
Deepak AFernández-Baca DTirthapura SSanderson MMcmahon M(2014)EvoMinerKnowledge and Information Systems10.5555/2687513.268757441:3(559-590)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.5555/2687513.2687574
Anh Nguyen VYamamoto A(2012)Mining of closed frequent subtrees from frequently updated databasesIntelligent Data Analysis10.5555/2595532.259554016:6(953-967)Online publication date: 1-Nov-2012
https://dl.acm.org/doi/10.5555/2595532.2595540
Jiménez ABerzal FCubero J(2012)Using trees to mine multirelational databasesData Mining and Knowledge Discovery10.1007/s10618-011-0218-x24:1(1-39)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.1007/s10618-011-0218-x
Selmaoui-Folcher NFlouvat F(2011)How to use "classical" tree mining algorithms to find complex spatio-temporal patterns?Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II10.5555/2033546.2033558(107-117)Online publication date: 29-Aug-2011
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Jiménez ABerzal FCubero J(2011)Mining patterns from longitudinal studiesProceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II10.1007/978-3-642-25856-5_13(166-179)Online publication date: 17-Dec-2011
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Okada MIshii NTorii I(2010)Information extraction using XPathProceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III10.5555/1885450.1885466(104-112)Online publication date: 8-Sep-2010
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