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A Method for Selecting Suitable Records Based on Fuzzy Conformance and Aggregation Functions

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Computational Intelligence (IJCCI 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 893))

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Abstract

Searching for suitable entities in a datasets is still a challenging task, because the entities’ attributes are often expressed by various data types including numerical, categorical, and fuzzy data. In addition, an attribute in a dataset may convey different data types for diverse records. In the query, the user may explain requirements by a different data type comparing with the one stored in a dataset, i.e. by linguistic term(s), whereas the respective attribute in a dataset is recorded as a real number and vice versa. Further, the user may provide complex preferences among atomic conditions. In this paper, we propose a robust framework capable to manage user requirements and match them with records in a dataset. The former is solved by the conformance measure, whereas for the latter different aggregation functions belonging to the conjunctive, averaging and hybrid classes have been suggested to cover particular aggregation needs like coalitions among atomic predicates and quantified conditions. The proposed method can be applied for selecting suitable records from any dataset containing numerical, categorical, fuzzy and binary data. The important characteristic of the presented method is the efficient applicability in the mentioned data collections, because conformance measure can manage different data types in the same manner. Finally, we discuss benefits, drawbacks and outline further activities.

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Acknowledgements

This paper was partially supported by the project VEGA-1/0373/18 entitled “Big data analytics as a tool for increasing the competitiveness of enterprises and supporting informed decisions” by the Ministry of Education, Science, Research and Sport of the Slovak Republic

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Authors and Affiliations

  1. Vlatacom Institute of High Technologies, 5 Milutina Milankovića Blvd, Belgrade, Serbia

    Miljan Vučetić

  2. Faculty of Economic Informatics, University of Economics, Dolnozemská cesta 1, Bratislava, Slovakia

    Miroslav Hudec

Authors
  1. Miljan Vučetić
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  2. Miroslav Hudec
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Corresponding author

Correspondence to Miljan Vučetić .

Editor information

Editors and Affiliations

  1. IUT Sénart, University Paris-Est Cretei, Lieusaint, France

    Christophe Sabourin

  2. Computer Architecture and Technology, University of Granada, Granada, Spain

    Juan Julián Merelo

  3. ETSI Informática, Sevilla, Spain

    Alejandro Linares Barranco

  4. University of Paris-EST Créteil (UPEC), Créteil, France

    Kurosh Madani

  5. University of Reading and Coventry University, Coventry, UK

    Kevin Warwick

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Vučetić, M., Hudec, M. (2021). A Method for Selecting Suitable Records Based on Fuzzy Conformance and Aggregation Functions. In: Sabourin, C., Merelo, J.J., Barranco, A.L., Madani, K., Warwick, K. (eds) Computational Intelligence. IJCCI 2018. Studies in Computational Intelligence, vol 893. Springer, Cham. https://doi.org/10.1007/978-3-030-64731-5_1

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