Multivariate data analysis method

摘要

This invention is a computerized method which unites a multivariate dataset and then performs various operations, including data analytics. The set is stored in a “bipartite synthesis matrix” (BSM), e.g., a rectangular matrix with rows of data objects and columns of variable attributes, defined by a plurality of partitions (each with a numerical range and a characteristic scale). Links within the matrix between data objects and attribute(s) are based on shared correspondences within partitions. The process exploits mode reduction in which shared correspondences of a BSM (or its graph) interrelate data objects by producing an adjacency matrix or its associated graph. The partition scale is repeatedly and incrementally altered, varying the density of shared correspondences within the data, based on partition number and size; therefore, a fully connected and weighted unipartite network may be established. Shared correspondences' given scale and variable attribute provide distance metrics for edges within the network.

主权项

1. A method of analyzing data by use of a programmable controller including software comprising computer instructions stored on non-transitory computer media for performing the steps of:
inputting a data set comprising a series of data objects each of which depend on at least one variable; creating a bipartite data synthesis matrix comprising a table with at least one row corresponding to said at least one variable, and columns defined by a plurality of partitions fitting within an interval according to an adjustable scale; populating said bipartite data synthesis matrix with said data set;
incrementally changing the adjustable scale of the columns of said bipartite data synthesis matrix to achieve aggregation of said data within the bipartite matrix, thereby establishing both absolute and relative proximity of data objects within a framework of said the adjustable scale and progressively aggregating or disaggregating data correspondences; applying a filter to selectively identify a significant subset of said data correspondences; populating a plurality of adjacency matrices, each said adjacency matrix being populated from said bipartite data synthesis matrix with data objects having significant data correspondences at each of said incremental scales.