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Additional resources for New Developments in Classification and Data Analysis: Proceedings of the Meeting of the Classification and Data Analysis Group (CLADAG) (Studies in Classification, Data Analysis, and Knowledge Organization)
Indicates the dissimilarity between T l and the observed set of trees T; the tree T,' with the minimum value of V(T,') will be the consensus tree T,. The number of trees considered in this search depends on m and r,,,, so the solution identified by the algorithm will be optimal only if the number of observed trees is high and/or their paths are long. For this reason, we have modified the algorithm previously proposed to extend the search to more trees. This purpose can be pursued in different ways.
The minimum test set error rate is related to the tree with the longest path (level) equal to 2 and the minimum value of the mean objective function. It is important to underline that these different results depend on only one of the 51 observations belonging to the test set. The three consensus trees identified at the second level differ only on the value of the variable involved in the first split, while the consensus trees identified at the next level are extensions of the second tree of the previous one.
In order t o correctly consider the possible solutions for the reconstruction of missing information, it is necessary t o separate the missing data characterization criteria (by using the Edit of the compatibility plane, that is t o say association rules among variables) from the information reconstruction criteria (by using the more effective Imputation met hods). T h e proposed strategy is based on Classification and Discrimination methods conducted on symbolic data and it enables us to extract both compatibility rules and t o impute data in order t o reconstruct the information.