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On Concise Representations of Frequent Patterns Admitting Negation

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Advances in Machine Learning II

Part of the book series: Studies in Computational Intelligence ((SCI,volume 263))

Abstract

The discovery of frequent patterns is one of the most important issues in the data mining area. An extensive research has been carried out for discovering positive patterns, however, very little has been offered for discovering patterns with negation. One of the main difficulties concerning frequent patterns with negation is huge amount of discovered patterns. It exceeds the number of frequent positive patterns by orders of magnitude. The problem can be significantly alleviated by applying concise representations that use generalized disjunctive rules to reason about frequent patterns, both with and without negation. In this paper, we examine three types of generalized disjunction free representations and derive the relationships between them. We also present two variants of algorithms for building such representations. The results obtained on a theoretical basis are verified experimentally.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Marzena Kryszkiewicz & Henryk Rybiński

  2. Institute of Electrical Apparatus, Technical University of Lodz, Stefanowskiego 18/22, 90-924, Lodz, Poland

    Katarzyna Cichoń

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  1. Marzena Kryszkiewicz
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  2. Henryk Rybiński
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  3. Katarzyna Cichoń
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Editors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, ul. Ordona 21, 01-237, Warsaw, Poland

    Jacek Koronacki  & Sławomir T. Wierzchoń  & 

  2. Woodward Hall 430C, University of North Carolina, 9201 University City Blvd., N.C. 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Systems Research Institute, Polish Academy of Sciences, ul.Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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Kryszkiewicz, M., Rybiński, H., Cichoń, K. (2010). On Concise Representations of Frequent Patterns Admitting Negation. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning II. Studies in Computational Intelligence, vol 263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05179-1_13

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  • DOI: https://doi.org/10.1007/978-3-642-05179-1_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05178-4

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