MULTIVARIATE DATA ANALYSIS METHOD

• US 20170046392A1
• Filed: 08/23/2016
• Published: 02/16/2017
• Est. Priority Date: 10/11/2012
• Status: Abandoned Application

• Current Assignee: Scott E. Lilienthal
• Original Assignee: Scott E. Lilienthal
• Inventors: Lilienthal, Scott E.
• Application Number:
  US15/244,620
• Publication Number:
  US 20170046392A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/221 Column-oriented storage; Management thereof
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/24554 Unary operations; Data partitioning operations
• CPC Class Codes: G06F 17/153 Multidimensional correlation or convolution
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 2207/58 Indexing scheme relating to groups G06F7/58 - G06F7/588
• CPC Class Codes: G06F 7/58 Random or pseudo-random number generators

Abstract

This invention is a computerized method which unites 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. Data objects are linked to one or more attributes within the matrix based on shared correspondences that occur within attribute 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.

Claims

1. A method for generating random numbers using a programmable controller including software comprising computer instructions stored on non-transitory computer media for performing the steps of:

generating binary data using a pseudorandom number generator;

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;

generating a random number;

determining a scale for said partitions based on said random number; and

populating said bipartite data synthesis matrix with said binary data.

2. 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;

identifying data correspondence based on the aggregated data within the bipartite matrix synthesis matrix;

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.