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Concept Based Lattice Mining (CBLM) Using Formal Concept Analysis (FCA) for Text Mining

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Book cover Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 520))

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Abstract

Extracting relevant resources according to a query is imperative due to the factors of time and accuracy. This study proposes a model that enables query matching using output lattices from Formal Concept Analysis (FCA) tool, based on Graph Theory. The deployment of FCA concept lattices ensures that the matching is done based on extracted concepts; not just mere keywords matching hence producing more relevant results. The focus of this study is on the method of Concept Based Lattice Mining (CBLM) where similarities among output lattices will be compared using their normalized adjacency matrices, utilizing a distance measure technique. The corresponding trace values obtained determines the degree of similarities among the lattices. An algorithm for CBLM is proposed and preliminary experimentation demonstrated promising results where lattices that are more similar have smaller trace values while higher trace values indicates greater dissimilarities among the lattices.

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Notes

  1. 1.

    A directed graph where the constellation of the relevant attribute values responsible for the position of the object is exhibited.

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Acknowledgements

This research is partially funded by the Centre of Research and Innovation Management, Universiti Sultan Zainal Abidin, Malaysia.

Author information

Authors and Affiliations

  1. Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA), Terengganu, Malaysia

    Hasni Hassan & Mumtazimah Mohamad

  2. School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu (UMT), Terengganu, Malaysia

    Md. Yazid Mohd Saman & Zailani Abdullah

Authors
  1. Hasni Hassan
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  2. Md. Yazid Mohd Saman
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  3. Zailani Abdullah
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  4. Mumtazimah Mohamad
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Corresponding author

Correspondence to Zailani Abdullah .

Editor information

Editors and Affiliations

  1. School of Information Technology, Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

  2. Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Malaysia

    Mohamed Othman

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Rozaida Ghazali

  4. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Mustafa Mat Deris

  5. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Hairulnizam Mahdin

  6. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

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Hassan, H., Mohd Saman, M.Y., Abdullah, Z., Mohamad, M. (2019). Concept Based Lattice Mining (CBLM) Using Formal Concept Analysis (FCA) for Text Mining. In: Abawajy, J., Othman, M., Ghazali, R., Deris, M., Mahdin, H., Herawan, T. (eds) Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) . Lecture Notes in Electrical Engineering, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-13-1799-6_9

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