The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Journal of Global Optimization
Using adaptive neuro-fuzzy inference system for hydrological time series prediction
Applied Soft Computing
POP algorithm: Kernel-based imputation to treat missing values in knowledge discovery from databases
Expert Systems with Applications: An International Journal
Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Information Sciences: an International Journal
A new method for design and reduction of neuro-fuzzy classification systems
IEEE Transactions on Neural Networks
Knowledge discovery by a neuro-fuzzy modeling framework
Fuzzy Sets and Systems
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
An adaptive neuro-fuzzy system for automatic image segmentation and edge detection
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
ANCFIS: A Neurofuzzy Architecture Employing Complex Fuzzy Sets
IEEE Transactions on Fuzzy Systems
Granular neural networks for numerical-linguistic data fusion and knowledge discovery
IEEE Transactions on Neural Networks
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In the age of rapidly increasing volumes of data, human experts have come to the urgent need to extract useful information from the huge amount of data. Knowldege discovery in databases has obtained much attention for researches and applications in business and in science. In this paper, we present a neuro-fuzzy approach using complex fuzzy sets (CFSs) for the problem of knowledge discovery. A CFS is an advanced fuzzy set, whose membership is complex-valued and characterized by an amplitude function and a phase function. The application of CFSs to the proposed complex neuro-fuzzy system (CNFS) can increase the functional mapping ability to find missing data for knowledge discovery. Moreover, we devise a hybrid learning algorithm to evolve the CNFS for modeling accuracy, combining the artificial bee colony algorithm and the recursive least squares estimator method. The proposed approach to knowledge discovery is tested through experimentation, whose results are compared with those by other approaches. The experimental results indicate that the proposed approach outperforms the compared approaches.