System and methods for analysis of data

• US 10,275,500 B2
• Filed: 09/27/2013
• Issued: 04/30/2019
• Est. Priority Date: 09/27/2012
• Status: Active Grant

• Current Assignee: Cornell University
• Original Assignee: Cornell University
• Inventors: Chattopadhyay, Ishanu, Lipson, Hod
• Primary Examiner(s): Chbouki, Tarek
• Application Number:
  US14/431,131
• Publication Number:
  US 20150242469A1
• Time in Patent Office: 2,041 Days
• Field of Search: None
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 16/319 Inverted lists
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• 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 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 2218/00 Aspects of pattern recognition specially adapted for signal processing

Abstract

Data processing including a universal metric to quantify and estimate the similarity and dissimilarity between data sets. Data streams are perfectly annihilated by a correct realization of their anti-streams. Any deviation of the collision product from a baseline, for example flat white noise, quantifies statistical dissimilarity. The invention relates generally to data mining. More specifically, the invention relates to the analysis of data using a universal metric to quantify and estimate the similarity and dissimilarity between sets of data.

Claims

1. A computer method for analyzing two or more data sets using algorithmic components, comprising the steps of:

generating a first sample data set from a hidden stochastic source including the steps of;

generating an independent stream ω

₀ from flat white noise;

reading a first symbol σ

₁ from a first symbol stream s₁ and a second symbol σ

₂ from the independent stream ω

₀, wherein each of the first symbol σ

₁ and the second symbol σ

₂ are one selected from the group comprising;

a letter, a number, a digit, a character, a sign, a figure, a mark, an icon, an image, a vector, a matrix, and a polynomia;

writing the first symbol stream s₁ to an output inverted stream s′

if the first symbol σ

₁ is equal to the second symbol σ

₂;

generating a second sample data set from an inverse hidden stochastic source including the steps of;

generating a number of |Σ

|−

1 independent copies of the first symbol stream s₁, wherein |Σ

| is a binary alphabet size consisting of the numbers 0 and 1;

reading a current symbol σ

_i from an inverse symbol stream s_i, wherein i=1, . . . , |Σ

|−

1;

writing

2. The computer method for analyzing data according to claim 1, wherein the data of the two or more data sets is sensor data.

3. The computer method for analyzing data according to claim 2, wherein the data of the two or more data sets is electroencephalographic (EEG) data.

4. The computer method for analyzing data according to claim 2, wherein the data of the two or more data sets is digital stethoscope data.

5. The computer method for analyzing data according to claim 2, wherein the data of the two or more data sets is photometry data.