LEAST SQUARE CLUSTERING AND FOLDED DIMENSION VISUALIZATION

• US 20080126464A1
• Filed: 07/02/2007
• Published: 05/29/2008
• Est. Priority Date: 06/30/2006
• Status: Abandoned Application

• Current Assignee: Shahin Movafagh Mowzoon
• Original Assignee: Shahin Movafagh Mowzoon
• Inventors: Mowzoon, Shahin Movafagh
• Application Number:
  US11/772,814
• Publication Number:
  US 20080126464A1
• Time in Patent Office: Days
• US Class Current: 708/442
• CPC Class Codes: G06F 18/40 Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor

Abstract

A two dimensional rendition of a multi-dimensional data set is presented wherein the multi-dimensional data set is graphed on a coordinate system having axes that are a predetermined angle away from each other axes in the coordinate system. Each subsequent predetermined angle may be half the previous predetermined angle for the series of coordinate axes. Additionally a clustering approach is presented that clusters the solution vectors of the data thereby combining elements of regression with clustering and reducing the dimensionality of the data to be clustered while allowing the clustering to be done against a set of reference vectors or data.

Claims

1. A method comprising:

receiving an input variable having multiple dimensions in a first coordinate system;

using an algorithm to convert the input variable from the first coordinate system to a second coordinate system; and

rendering a two-dimensional visual representation of the input variable using the second coordinate system, wherein the second coordinate system has a series of coordinate axes in a single plane each located at a corresponding predetermined angle away from each of the other coordinate axes of the second coordinate system.

2. The method as defined in claim 1, wherein the algorithm to convert the input variable includes multiplying each coordinate of the input variable by a complex number.

3. The method as defined in claim 2, wherein the complex number that is used to multiply the first dimension of the input variable is the number negative one.

4. The method as defined in claim 1, wherein the series of coordinate axes includes:

a first coordinate axis in a plane;

a second coordinate axis in the plane that is located at one of the corresponding predetermined angles away from the first coordinate axis; and

a third coordinate axis in the plane that is located at half the value of the one of the corresponding predetermined angles away from the second coordinate axis.

5. The method as defined in claim 1, wherein a first coordinate in the series of coordinate axes is forty-five degrees away from a second coordinate axis in the series of coordinates axes and the second coordinate axis in the series of coordinate axes is immediately previous to the first coordinate axis.

6. The method as defined in claim 1, wherein:

each coordinate axis in the series of coordinate axes is each located in a single plane;

each coordinate axis in the series of coordinate axes is each located at the corresponding predetermined angle away from the previous coordinate axis;

each subsequent corresponding predetermined angle is equal to half the angular distance of the immediately previous corresponding predetermined angle.

7. The method as defined in claim 6, wherein the first corresponding angle is about ninety degrees.

8. The method as defined in claim 1, further comprising:

applying a second algorithm to the input variable, wherein each of the dimensions of the input variable is grouped based on a trait.

9. A method for detecting variations in a spectra signal, comprising:

using an algorithm to convert the spectra signal from the first coordinate system to a second coordinate system;

rendering a two-dimensional visual representation of the spectra signal using the second coordinate system, wherein the second coordinate system has a series of coordinate axes in a single plane each located at a corresponding predetermined angle away from each of the other coordinate axes of the second coordinate system;

comparing each two-dimensional visual representation for each of the corresponding spectra signal with each other; and

grouping each two-dimensional visual representation for each of the corresponding spectra signal into a plurality of set of two-dimensional visual representation based on a common visual characteristics.