Based on the American Time Use Survey (ATUS), the proposed approach integrates k-modes clustering and demographic-based probability neural networks and identifies 10 distinctive behavior patterns.
Demographics clustered by age groups with patients' information of age, gender, weight, body size, and injected activity.
While they use a random allocation of LCTs, we compare a random allocation with a clustered one using socio-demographic information, although here the focus is the load profile impact.
It also examined the clustering of risk behavioursand the association between the clusters and depressive symptoms and between the clusters and demographic variables (i.e. gender, ethnicity, age, and being a parent).
Compared with the Meyer et al. (2014) clusters, their "demographic and personality determinants of entrepreneurship", and "eclectic approaches on entrepreneurship" clearly appear in the pattern (3a).
Here we compare estimators based on two models, one that uses simple geographic clustering and demographic data and one that uses more elaborate covariate information that relates subsets to one another.
John Drzik, the chief executive of Oliver Wyman Group and one of the report's lead authors, said a "cluster of demographic and fiscal trends" were "colliding to create future severe risks".
In the first cluster, social demographic variables, marital status and duration of employment were associated with annual hospitalization (Table 4).
In the first cluster (social demographic), all variables were not significantly associated with two-week physician visit (Table 3).
To derive the predictive ability of the risk factor clusters after demographic variables had been accounted for the variables were entered in two blocks: age and sex were entered in the first block while risk factor clusters were entered in the second block.
