Metallurgical design of microalloyed steel used to be a challenge due to its multivariate nature. Over ten kinds of microalloying elements and multi-step processing routes have complex interactions and different contributions to the final mechanical properties. Data-driven model is able to throw a rapid insight into the composition-processing -property correlation of steel metallurgy in a syste...

DNA microarray data have provided us with the opportunity to assess the expression levels for thousands of genes simultaneously. One of the uses of this information is to classify cancer tumors. A noted challenge in using microarray information is analytical. Following the work of Zhang et al. (1), we further pursue the use of recursive partitioning in analyses of microarray data for cancer cla...

Although a number of predictors of adherence to mammography screening guidelines have been identified using traditional statistical methods, many women are not screening according to these guidelines. Recursive partitioning may aid in developing novel intervention strategies to promote this screening behavior by identifying subgroups of women that differ on adherence across predictor variables....

In a non-strict language, functions may return values before their arguments are available, and data structures may be defined before all their components are defined. Compiling such languages to conventional hardware is not straightforward; instructions do not have a fixed compile time ordering. Such an ordering is necessary to execute programs efficiently on current microprocessors. Partition...

Glioblastoma accounts for more than half of diffuse gliomas. The prognosis of patients with glioblastoma remains poor despite comprehensive and intensive treatments. Furthermore, the clinical significance of molecular parameters and routinely available clinical variables for the prognosis prediction of glioblastomas remains limited. The authors describe a novel model may help in prognosis predi...

Recursive partitioning is an analytic technique that is useful for identifying complex combinations of conditions that predict particular outcomes as well as for delineating multiple subgroup differences in how such factors work together. As such, the methodology is well suited to multidisciplinary, life course inquiry in which the goal is to integrate many interacting influences and understand...

Although a number of predictors of adherence to mammography screening guidelines have been identified using traditional statistical methods, many women are not screening according to these guidelines. Recursive partitioning may aid in developing novel intervention strategies to promote this screening behavior by identifying subgroups of women that differ on adherence across predictor variables....

The problem of partitioning a graph such that the number of edges incident to vertices in diierent partitions is minimized, arises in many contexts. Some examples include its recursive application for minimizing ll-in in matrix factorizations and load-balancing for parallel algorithms. Spectral graph partitioning algorithms partition a graph using the eigenvector associated with the second smal...

We describe a non-parametric estimator for the differential entropy of a multidimensional distribution, given a limited set of data points, by a recursive rectilinear partitioning. The estimator uses an adaptive partitioning method and runs in Θ ( N log N ) time, with low memory requirements. In experiments using known distributions, the estimator is several orders of magnitude faster than othe...