File Entry: A Parallelizing Interface for K-Means Type Clustering Algorithms and Neural Network Batch Training. Seminar on Artificial Intelligence Applications in Industry (AIAI'03).

 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 environment. To alleviate this task, we present a data parallel interface called Distributed Data Partitioning Interface (DDPI). Its simple interface permits parallel implementation of k-means type clustering algorithms and neural network batch training even by users with little understanding of parallel computing technicalities. In this work we demonstrate that it is possible to achieve near ideal speedups when k-means and k-harmonic means clustering algorithms and multilayer perceptron batch training are parallelized with DDPI.