Title: Joint similarity measures defined with quasi-arithmetic means
Authors: Etienne Cuvelier - ICHEC - Brussel (Belgium) [presenting]
Abstract: A lot of data analysis methods are based on similarity or dissimilarity measures but, most of the times, these measures are defined for one type of data (real multidimensional data, interval data, functional data, ...). This fact implies that all the techniques of knowledge extraction based on such measures can be performed only on the data type for which they are defined. But the description and the modelling of real situations require the joint use of several kind of data. We propose a new technique of combination of different measures in one single result. The method is based on Quasi-Arithmetics Means using Archimedean Generators. Quasi-Arithmetic Means with this kind of generators have several advantages to compute a resulting measure starting from several measures (computed on different types of data describing the same concept or individual): they allow to choose to emphasize the similarity or the dissimilarity between objects, they have flexible parameters,its possible to mix similarities and dissimilarities to compute a resulting similarity (or dissimilarity). The resulting measure (similarity or dissimilarity) can be used in any existing algorithm based on such measures: clustering, supervised classification and so on. We will give some examples of use of this method data mixing functional data and other types of data.