research-article

Closed patterns meet n-ary relations

Authors:

Jérémy Besson,

Céline Robardet,

Jean-François BoulicautAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 3, Issue 1

Article No.: 3, Pages 1 - 36

https://doi.org/10.1145/1497577.1497580

Published: 23 March 2009 Publication History

Abstract

Set pattern discovery from binary relations has been extensively studied during the last decade. In particular, many complete and efficient algorithms for frequent closed set mining are now available. Generalizing such a task to n-ary relations (n ≥ 2) appears as a timely challenge. It may be important for many applications, for example, when adding the time dimension to the popular objects × features binary case. The generality of the task (no assumption being made on the relation arity or on the size of its attribute domains) makes it computationally challenging. We introduce an algorithm called Data-Peeler. From an n-ary relation, it extracts all closed n-sets satisfying given piecewise (anti) monotonic constraints. This new class of constraints generalizes both monotonic and antimonotonic constraints. Considering the special case of ternary relations, Data-Peeler outperforms the state-of-the-art algorithms CubeMiner and Trias by orders of magnitude. These good performances must be granted to a new clever enumeration strategy allowing to efficiently enforce the closeness property. The relevance of the extracted closed n-sets is assessed on real-life 3-and 4-ary relations. Beyond natural 3-or 4-ary relations, expanding a relation with an additional attribute can help in enforcing rather abstract constraints such as the robustness with respect to binarization. Furthermore, a collection of closed n-sets is shown to be an excellent starting point to compute a tiling of the dataset.

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Index Terms

Closed patterns meet n-ary relations
1. Information systems
  1. Information systems applications
    1. Data mining
2. Theory of computation
  1. Design and analysis of algorithms

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 3, Issue 1

March 2009

251 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/1497577

Issue’s Table of Contents

Copyright © 2009 ACM.

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Association for Computing Machinery

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Publication History

Published: 23 March 2009

Accepted: 01 November 2008

Revised: 01 October 2008

Received: 01 April 2008

Published in TKDD Volume 3, Issue 1

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Veyrin-Forrer LKamal ADuffner SPlantevit MRobardet C(2024)On GNN explainability with activation rulesData Mining and Knowledge Discovery10.1007/s10618-022-00870-z38:5(3227-3261)Online publication date: 1-Sep-2024
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Huynh VFürnkranz JBeck F(2023)Efficient learning of large sets of locally optimal classification rulesMachine Language10.1007/s10994-022-06290-w112:2(571-610)Online publication date: 23-Jan-2023
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