Hierarchical Capital Allocation Using Clustered Machine Learning

摘要

A cluster of server computing devices receives a matrix of observations and divides the matrix into a plurality of input data sets. Each processor in the cluster generates a first data structure for a distance matrix based upon a corresponding input data set, the distance matrix comprising a plurality of items, and clusters the items to generate a clustered distance matrix. Each processor generates a second data structure for a linkage matrix using the clustered matrix. Each processor analyzes the linkage matrix to determine a number of items per cluster and analyzes the linkage matrix to assign a weight to each cluster based upon a distance of the cluster to other clusters and a size of the cluster. Each processor generates a third data structure containing the clusters and assigned weights. Each third data structure is consolidated into a hierarchical data structure, which is transmitted to a remote computing device.