A k-means Consensus Classification

The aim of this paper is to detect social and territorial structures from the census data of the administrative units in Italy. Variables describing each administrative unit can be divided in three distinguishable groups. Three approaches to analyze these data are proposed and compared. The first two approaches are essentially two-step ‘tandem analysis’ consisting of a preliminary factorial analysis and then a subsequent cluster analysis on the first few factors. The third approach suggests three different classifications, one for each group of variables, and then establishes a consensus classification via the k-means-clustering criterion.