We combine ordering-based approaches with the ALG structure to identify multiple clusters in case-control datasets, in a fivestep procedure. In the first step, we subdivide adaptively the domain into square cells (blue squares in Fig. 1 ), with controls (pink points) and cases (little dotted black squares). In the second step, the cell's ordering given by the Hilbert curve is used to sequentially join cells with the highest proportion of cases over controls. This produces loose groups of higher than average rates of disease. The adjacent groups thus formed are themselves united, according to certain criteria, into larger groups in the third step, forming the cluster candidates. In the fourth step, Kulldorff's spatial scan statistic is computed for each group, and the clusters are ranked. Finally the cases are randomized, and steps 1Á4 are repeated. That is done hundreds of times to compute the significance of each cluster. Only significant clusters are reported.