Unsupervised Learning of Fuzzy Association Rules for Consumer Behavior Modeling
Albert Orriols-Puig, Jorge Casillas, Francisco José Martínez-López · 2009
Marketing-oriented firms are especially concerned with modeling consumer behavior to improve their information and aid their decision processes on markets. For this purpose, marketing experts use complex models and apply statistical methodologies to infer conclusions from data. Recently, the application of machine learning has been detected as a promising approach to complement these classical techniques of analysis. In this paper, we follow this idea and propose a system, addressed as Fuzzy-CSar, to extract fuzzy association rules from certain consumption problem analyzed. But, as a differentiating sign of identity from other methods, Fuzzy-CSar does not assume any aprioristic causality (so model) within the variables forming the consumer database. Instead, the system is responsible for extracting the strongest associations among variables, and so, the structure of the problem. Fuzzy-CSar is applied to a real-world marketing problem and the results are compared with those obtained by a multi-objective genetic fuzzy system expressly designed for this marketing problem. The results show the advantages of evolving fuzzy association rules and the competitiveness of Fuzzy-CSar in general. 1