Optimal scaling trees

The framework of this paper is supervised statistical learning in data mining. A typical data-mining problem is to deal with large sets of within-groups correlated inputs compared to the number of observed objects. In case of complex relationships standard tree-based procedures offer unstable and not ever interpretable solutions. For that multiple splits defined upon a suitable combination of inputs are required. This paper provides a solution to build up a tree-based method whose nodes splitting is due to factorial multiple splitting categorical variables. A recursive partitioning algorithm is introduced considering a two-stage splitting criterion based on object scores from Nonlinear canonical correlation analysis

Kemeny's axiomatic approach to find consensus ranking in tourist satisfaction

The tourist satisfaction aims at measuring the customer satisfaction of a particular kind of service: tourism. In order to either detect the tourists satisfaction or point out the best possible alternatives to meet their needs, they have been asked to order a series of actions sorted by dimension. The collected data have been processed through an axiomatic vision introduced by Kemeny, because we consider ex-aequo well weighted choices instead of non-choices. Moreover a classical comparison with Borda methos is provided for the more interesting Dimensions

3Way Trees.

This paper provides a tree-based methodology to deal with three-way data sets. The aim is to partition cases on the basis of a set of attributes measured in various situations. A supervised approach is considered, thus the recursive partitioning criterion takes account of the internal homogeneity of the response variable. The proposed classification and regression tree-based methodology can be extended to multivariate response variables as well. In the following, the general framework is introduced and some special cases are briefly described. The results of an application on a real data set are summarized