The k-means clustering algorithm and neural network batch training becomes computationally intensive when the manipulated data is large. One way to reduce the computational demand of such techniques is to execute them in a concurrent manner. Unfortunately, the effort required to implement these techniques in a distributed computing environment remains daunting. Much of the work takes place when partitioning and distributing workloads over processors in the distributed computing environm...