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Granular cognitive maps: a review

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Abstract

In this paper, we survey different granular computing (GrC) applications to the field of cognitive mapping by highlighting how fuzzy cognitive maps (FCMs) have been augmented with different types of information granules such as intervals, fuzzy sets, fuzzy clustering, rough sets, and grey sets. These information granules have been integrated into core FCM components such as their set of concept nodes, the causal links among these concepts or their underlying inference mechanisms. We discuss the advantages and limitations brought forth by these granular cognitive maps (GCMs) as well as their reported applications, with especial emphasis on time series analysis and pattern classification scenarios. To the best of our knowledge, this is the first time that GCMs stemming from a variety of granular constructs are systematically reviewed. We hope this survey inspires further research endeavors in the exciting interplay between GrC and intelligent systems.

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Authors and Affiliations

  1. Research and Engineering Division, Larus Technologies Corporation and School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward Ave., Ottawa, ON, K1N 6N5, Canada

    Rafael Falcon

  2. Faculty of Business Economics, Universiteit Hasselt, Agoralaan gebouw D, 3590, Diepenbeek, Belgium

    Gonzalo Nápoles & Koen Vanhoof

  3. Computer Science Department, Central University of Las Villas, Carretera Camajuaní km 5.5, Santa Clara, Villa Clara, Cuba

    Rafael Bello

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  1. Rafael Falcon
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  2. Gonzalo Nápoles
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Correspondence to Rafael Falcon.

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Falcon, R., Nápoles, G., Bello, R. et al. Granular cognitive maps: a review. Granul. Comput. 4, 451–467 (2019). https://doi.org/10.1007/s41066-018-0104-7

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