Ricerc@Sapienza

In most situations, we would like to recognize specific patterns or subgroups in a set of observations about
individuals. Therefore, the general goal would be to recognize in a massive dataset some common features of subgroups of individuals that could be relevant for understanding collective behaviour and predictive possible future behaviour. One of the main methodology dealing with such a problem is ¿cluster analysis¿. Inside the literature on cluster, a special role is played by those techniques that provide a "soft" (i.e. fuzzy) representation of the information. Fuzzy clustering algorithms are, concisely, models for assigning to each individual his/her membership degree to a particular subgroup of the population. Apart from possible computational advantages , their main convenience is grounded on the fact that they provide: a natural way of encoding and represents relevant information (which can also be expressed with vagueness and imprecision); a realistic representation of individual (e.g. consumer) behaviour that cannot be described by single numbers, but requires a more general specification (the so-called fuzzy set or fuzzy number representation).In this research project, we aim at developing specific theoretical, computational and applied investigations about the use of fuzzy clustering methods in order to deal with several
problems arising in marketing and consumer behaviour. Our final aim is to contribute to the development of practical tools that may help the decision makers (e.g., managers) to understand the behaviour of consumers.
The expected output of the project is the preparation of scientific papers to be submitted to international journals and the dissemination of the results to the international scientific community. Moreover, the participants will analyse the possibility of exploitation of the developed models in terms of novel knowledge and possible policy implications.