Date Added: Sep 2012
Hierarchical attributes appear in taxonomic or ontology based data (e.g. NACE economic activities, ICD-classified diseases, animal/ plant species, etc.). Such taxonomic data are often exploited as if they were flat nominal data without hierarchy, which implies losing substantial information and analytical power. The authors introduce marginality, a numerical mapping for taxonomic data that allows using on those data many of the algorithms and analytical techniques designed for numerical data. They show how to compute descriptive statistics like the mean, the variance and the covariance on marginality-mapped data.