Completeness and consistency conditions for learning fuzzy rules
Fuzzy Sets and Systems
Past, present, and future of decision support technology
Decision Support Systems - Special issue: Decision support systems: Directions for the next decade
Evolutionary neural network modeling for forecasting the field failure data of repairable systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Generating an interpretable family of fuzzy partitions from data
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Genetic learning of fuzzy rules based on low quality data
Fuzzy Sets and Systems
A highly adaptive recommender system based on fuzzy logic for B2C e-commerce portals
Expert Systems with Applications: An International Journal
Solving flexible flow-shop problem with a hybrid genetic algorithm and data mining: A fuzzy approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Global data mining: An empirical study of current trends, future forecasts and technology diffusions
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
The main problem currently faced by market-oriented firms is not the availability of information (data), but the possession of appropriate levels of knowledge to take the right decisions. This is common background for firms. In this regard, marketing professionals and scholars highlight the necessity for knowing and explaining consumers' behaviour patterns in an increasingly efficient way. The use of new knowledge discovery methods, able to exploit such data, may represent a relevant source of competitive advantage. In marketing, the information about most consumer variables of interest is usually obtained by means of questionnaires containing a diversity of items. It is also frequent that marketing modellers make use of unobserved variables to build the consumer models; i.e., abstract variables that need to be measured by means of a set of observed variables or items associated with them. In these cases, the value of a certain unobserved variable cannot be assigned to a number, but to a potentially scattered set of numbers. This fact disables the application of conventional data mining techniques to extract knowledge from them. In this paper, we present a new approach that is able to deal with this kind of uncertain data by using a multiobjective genetic algorithm to derive fuzzy rules. Specifically, we propose a complete methodology that considers the different stages of knowledge discovery: data collection, data mining, and knowledge interpretation. This methodology is experimented on a consumer modelling application in interactive computer-mediated environments.