Axiomatic Fuzzy Sets (AFS) theory facilitates a significant step on how to convert the information in the observed data into fuzzy sets (membership functions) and implement their fuzzy logic operations, which offers a far more flexible and powerful tool for representing human knowledge and emulating the human recognition process. AFS theory has gained increasing interest from various research fields recently. AFS has been extended for formal concept analysis, rough set, clustering, classification and other learning tasks. These newly proposed methods significantly boom the application of AFS theory. Due to its unique characteristic in semantic representation, AFS theory has been applied in many domains, such as business intelligence, computer vision, financial analysis, and clinical data analysis.

The code is implemented by Matlab. The Java and Python version of AFS is developing. We will publish that in the future. How to use this code can be found in "AFSBasicFunction_Help.pdf".

If you find AFS useful in your research, please consider to cite our paper:

@book{Liu2009Axiomatic,

  title={Axiomatic Fuzzy Set Theory and Its Applications},

  author={Liu, Xiaodong and Pedrycz, Witold},

  publisher={Springer Berlin Heidelberg},

  year={2009},
}

Copyright (c) 2017, Xiaodong Liu All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

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