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FCA-ARMM: A Model for Mining Association Rules from Formal Concept Analysis

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Recent Advances on Soft Computing and Data Mining (SCDM 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 549))

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Abstract

The evolution of technology in this era has contributed to a growing of abundant data. Data mining is a well-known computational process in discovering meaningful and useful information from large data repositories. There are various techniques in data mining that can be deal with this situation and one of them is association rule mining. Formal Concept Analysis (FCA) is a method of conceptual knowledge representation and data analysis. It has been applied in various disciplines including data mining. Extracting association rule from constructed FCA is very promising study but it is quite challenging, not straight forward and nearly unfocused. Therefore, in this paper we proposed an Integrated Formal Concept Analysis–Association Rule Mining Model (FCA-ARMM) and an open source tool called FCA-Miner. The results show that FCA-ARMM with FCA-Miner successful in generating the association rule from the real dataset.

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Acknowledgement

This work is supported by the research grant from Research Acceleration Center Excellence (RACE) of Universiti Kebangsaan Malaysia.

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Authors and Affiliations

  1. Faculty of Entrepreneurship and Business / Centre of Computing and Informatics, Universiti Malaysia Kelantan, 16100, Kota Bharu, Kelantan, Malaysia

    Zailani Abdullah

  2. School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030, Kuala Terengganu, Terengganu, Malaysia

    Md Yazid Mohd Saman & Basyirah Karim

  3. Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Tutut Herawan

  4. Faculty of Science Computer and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia

    Mustafa Mat Deris

  5. Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Abdul Razak Hamdan

Authors
  1. Zailani Abdullah
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  2. Md Yazid Mohd Saman
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  3. Basyirah Karim
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  4. Tutut Herawan
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  5. Mustafa Mat Deris
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  6. Abdul Razak Hamdan
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Corresponding author

Correspondence to Zailani Abdullah .

Editor information

Editors and Affiliations

  1. Department of Information System, University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Rozaida Ghazali

  3. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Nazri Mohd Nawi

  4. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Mustafa Mat Deris

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Abdullah, Z., Saman, M.Y.M., Karim, B., Herawan, T., Deris, M.M., Hamdan, A.R. (2017). FCA-ARMM: A Model for Mining Association Rules from Formal Concept Analysis. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_22

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  • DOI: https://doi.org/10.1007/978-3-319-51281-5_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51279-2

  • Online ISBN: 978-3-319-51281-5

  • eBook Packages: EngineeringEngineering (R0)

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