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. © 2007 Elsevier Ltd. All rights reserved.
Mendeley saves you time finding and organizing research
Choose a citation style from the tabs below