On statistically inference for fuzzy data with applications to descriptive statistics
Fuzzy Sets and Systems
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
On a class of fuzzy c-numbers clustering procedures for fuzzy data
Fuzzy Sets and Systems
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
A genetic integrated fuzzy classifier
Pattern Recognition Letters - Special issue: Advances in pattern recognition
Fundamentals of Statistics with Fuzzy Data (Studies in Fuzziness and Soft Computing)
Fundamentals of Statistics with Fuzzy Data (Studies in Fuzziness and Soft Computing)
Variable selection in clustering for marketing segmentation using genetic algorithms
Expert Systems with Applications: An International Journal
Fuzzy classifier design using genetic algorithms
Pattern Recognition
A methodology of determining aggregated importance of engineering characteristics in QFD
Computers and Industrial Engineering
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Using SOM and PCA for analysing and interpreting data from a P-removal SBR
Engineering Applications of Artificial Intelligence
Self organized mapping of data clusters to neuron groups
Neural Networks
A self-organized, distributed, and adaptive rule-based induction system
IEEE Transactions on Neural Networks
Genetic algorithm based framework for mining fuzzy association rules
Fuzzy Sets and Systems
A parametric model for fusing heterogeneous fuzzy data
IEEE Transactions on Fuzzy Systems
Two nonparametric models for fusing heterogeneous fuzzy data
IEEE Transactions on Fuzzy Systems
Principal component analysis of fuzzy data using autoassociative neural networks
IEEE Transactions on Fuzzy Systems
A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems
IEEE Transactions on Neural Networks
Controlling chaos by GA-based reinforcement learning neural network
IEEE Transactions on Neural Networks
A comparison of nonlinear methods for predicting earnings surprises and returns
IEEE Transactions on Neural Networks
A Hybrid Neurogenetic Approach for Stock Forecasting
IEEE Transactions on Neural Networks
Learning in linear neural networks: a survey
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
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In product design, various methodologies have been proposed for market segmentation, which group consumers with similar customer requirements into clusters. Central points on market segments are always used as ideal points of customer requirements for product design, which reflects particular competitive strategies to effectively reach all consumers' interests. However, existing methodologies ignore the fuzziness on consumers' customer requirements. In this paper, a new methodology is proposed to perform market segmentation based on consumers' customer requirements, which exist fuzziness. The methodology is an integration of a fuzzy compression technique for multi-dimension reduction and a fuzzy clustering technique. It first compresses the fuzzy data regarding customer requirements from high dimensions into two dimensions. After the fuzzy data is clustered into marketing segments, the centre points of market segments are used as ideal points for new product development. The effectiveness of the proposed methodology in market segmentation and identification of the ideal points for new product design is demonstrated using a case study of new digital camera design.