We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the clusters. The algorithms work by maximizing the dependence between the taxonomy and the original data. The resulting taxonomy is a more informative visualization of complex data than simple clustering; in addition, tak...

normal 0 false false false en-us x-none ar-sa microsoftinternetexplorer4 background: one of the major steps for planning and making polices in iranian health and health service sector is to gain information on the condition of such services in each province. thus, the present study intended to determine the degree of development and ranking of provinces at this sector. it can identify the undev...