IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i22p3590-d1522245.html
   My bibliography  Save this article

A Formal Fuzzy Concept-Based Approach for Association Rule Discovery with Optimized Time and Storage

Author

Listed:
  • Gamal F. Elhady

    (Faculty of Computers and Information, Menoufia University, Shebin El-Koom 32511, Egypt)

  • Haitham Elwahsh

    (Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan
    Department of Computer Science, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh 33511, Egypt)

  • Maazen Alsabaan

    (Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia)

  • Mohamed I. Ibrahem

    (School of Computer and Cyber Sciences, Augusta University, Augusta, GA 30912, USA)

  • Ebtesam Shemis

    (Faculty of Computers and Information, Menoufia University, Shebin El-Koom 32511, Egypt)

Abstract

Association Rule Mining (ARM) relies on concept lattices as an effective knowledge representation structure. However, classical ARM methods face significant limitations, including the generation of misleading rules during data-to-formal-context mapping and poor handling of heterogeneous data types such as linguistic, continuous, and imprecise data. This study aims to address these limitations by introducing a novel fuzzy data structure called the “fuzzy iceberg lattice” and its corresponding construction algorithm. The primary objectives of this study are to enhance the efficiency of extracting and visualizing frequent fuzzy closed item sets and to optimize both execution time and storage requirements. The necessity of this research stems from the high computational cost and redundancy associated with traditional fuzzy approaches, which, while capable of managing quantitative and imprecise data, are often impractical for large-scale applications in real scenarios. The proposed approach incorporates a ‘fuzzy min-max basis algorithm’ to derive exact and approximate rule bases from the extracted fuzzy closed item sets, eliminating redundancy while preserving valuable insights. Experimental results on benchmark datasets demonstrate that the proposed fuzzy iceberg lattice outperforms traditional fuzzy concept lattices, achieving an average reduction of 74.75% in execution time and 70.53% in memory usage. This efficiency gain, coupled with the lattice’s ability to handle crisp, quantitative, fuzzy, and heterogeneous data types, underscores its potential to advance ARM by yielding a manageable number of high-quality fuzzy concepts and rules.

Suggested Citation

  • Gamal F. Elhady & Haitham Elwahsh & Maazen Alsabaan & Mohamed I. Ibrahem & Ebtesam Shemis, 2024. "A Formal Fuzzy Concept-Based Approach for Association Rule Discovery with Optimized Time and Storage," Mathematics, MDPI, vol. 12(22), pages 1-17, November.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:22:p:3590-:d:1522245
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/22/3590/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/22/3590/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:22:p:3590-:d:1522245. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.