Data ranking with a lorentzian fuzzy score

• US 20070271261A1
• Filed: 06/22/2007
• Published: 11/22/2007
• Est. Priority Date: 09/12/2001
• Status: Abandoned Application

• Current Assignee: Science Applications International Corporation
• Original Assignee: Science Applications International Corporation
• Inventors: Lau, Yun-Tung, Nagy, Marton
• Application Number:
  US11/812,954
• Publication Number:
  US 20070271261A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2468 Fuzzy queries
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access
• CPC Class Codes: Y10S 707/99936 Pattern matching access

Abstract

The present invention relates to a method for searching a document database such as the Internet and ranking the results obtained from such a search. The invention also relates to ranking of a set of numerical data according to a set of user specified preferences, including target range, fuzziness and bias. A fuzzy score is calculated for each database record satisfying a query and the results ranked according to fuzzy score. The fuzzy score is calculated using a Lorentzian fuzzy score formula.

Claims

1. A method for ranking a plurality of elements, wherein a plurality of numerical parameters characterizes each element, the method comprising:

prior to ranking receiving an indication of at least one of fuzziness and bias as inputs to ranking;

ranking a plurality of elements based at least in part on an aggregate score;

the aggregate score based at least in part on a plurality of Lorentzian fuzzy scores;

each Lorentzian fuzzy score based at least in part on a numerical parameter characterizing each element, wherein the formula used to calculate each Lorentzian fuzzy score is determined at least in part based on the polarity of the bias input.

2. The method of claim 1, wherein at least one of the numerical parameters is a target range having a minimum numeric value and a maximum numeric value.

3. The method of claim 2, wherein the polarity of the bias input is negative and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:

$S<br><br>\left(x\right)=\begin{array}{cc}\frac{1}{1+{\left(\frac{x-x_{\min }}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>1}\right)}^2}& x<<br><br>x_{\min }\\ 1+\frac{\mathrm{\beta <br><br>}*\left(x-x_{\min }\right)}{x_{\max }-x_{\min }}& x_{\min }\le <br><br>x\le <br><br>x_{\max }\\ \frac{1+\mathrm{\beta <br><br>}}{1+{\left(\frac{x-x_{\max }}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>2}\right)}^2}& x_{\max }<<br><br>x\end{array}$

where x represents any data values, i.e., x ε

{x₁, . . . , x_k}.

4. The method of claim 2, wherein the polarity of the bias input is positive and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:

$S<br><br>\left(x\right)=\begin{array}{cc}\frac{1-\mathrm{\beta <br><br>}}{1+{\left(\frac{x-x_{\min }}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>1}\right)}^2}& x<<br><br>x_{\min }\\ 1+\frac{\mathrm{\beta <br><br>}*\left(x-x_{\max }\right)}{x_{\max }-x_{\min }}& x_{\min }\le <br><br>x\le <br><br>x_{\max }\\ \frac{1}{1+{\left(\frac{x-x_{\max }}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>2}\right)}^2}& x_{\max }<<br><br>x\end{array}$

where x represents any data values, i.e., x ε

{x₁, . . . , x_k}.

5. The method of claim 1, wherein the polarity of the bias input is negative and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:

$S<br><br>\left(x\right)=\begin{array}{cc}\frac{1}{1+{\left(\frac{x-\underset{\_}{x}}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>1}\right)}^2}& x<<br><br>\underset{\_}{x}\\ 1& x=\underset{\_}{x}\\ \frac{1+\mathrm{\beta <br><br>}}{1+{\left(\frac{x-\underset{\_}{x}}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>2}\right)}^2}& x><br><br>\underset{\_}{x}\end{array}$

where x represents any data values, i.e., x ε

{x₁, . . . , x_k}.

6. The method of claim 1, wherein the polarity of the bias input is positive and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:

$S<br><br>\left(x\right)=\begin{array}{cc}\frac{1-\mathrm{\beta <br><br>}}{\underset{\_}{1+{\left(\frac{x-\underset{\_}{x}}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>1}\right)}^2}}& x<<br><br>\underset{\_}{x}\\ 1& x=\underset{\_}{x}\\ \frac{1}{1+{\left(\frac{x-\underset{\_}{x}}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>2}\right)}^2}& x><br><br>\underset{\_}{x}\end{array}$

where x represents any data values, i.e., x ε

{x₁, . . . , x_k}.

7. The method of claim 1 wherein the aggregate score is based at least in part on summing the plurality of Lorentzian fuzzy scores.

8. The method of claim 1 wherein after calculating each Lorentzian fuzzy score, the aggregate score is based at least in part on weighted summing of the plurality of Lorentzian fuzzy scores.

9. The method of claim 8 further comprising:

prior to ranking, receiving an indication of weight for at least one parameter.

10. The method of claim 1 wherein at least one indication is quantitative.

11. The method of claim 1 wherein at least one indication is qualitative.

12. The method of claim 11 wherein each qualitative indication is mapped to a quantitative indication.

13. A method for ranking a plurality of elements, wherein a numerical parameter characterizes each element, the method comprising:

prior to ranking receiving an indication of at least one of fuzziness and bias as inputs to ranking;

ranking a plurality of elements based at least in part on a Lorentzian fuzzy score based at least in part on a numerical parameter characterizing each element, wherein the formula used to calculate each Lorentzian fuzzy score is determined at least in part based on the polarity of the bias input.

14. The method of claim 13, wherein at least one of the numerical parameters is a target range having a minimum numeric value and a maximum numeric value.

15. The method of claim 14, wherein the polarity of the bias input is negative and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:

$S<br><br>\left(x\right)=\begin{array}{cc}\frac{1}{1+{\left(\frac{x-x_{\min }}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>1}\right)}^2}& x<<br><br>x_{\min }\\ 1+\frac{\mathrm{\beta <br><br>}*\left(x-x_{\min }\right)}{x_{\max }-x_{\min }}& x_{\min }\le <br><br>x\le <br><br>x_{\max }\\ \frac{1+\mathrm{\beta <br><br>}}{1+{\left(\frac{x-x_{\max }}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>2}\right)}^2}& x_{\max }<<br><br>x\end{array}$

where x represents any data values, i.e., x ε

{x₁, . . . , x_k}.

16. The method of claim 14, wherein the polarity of the bias input is positive and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:

$S<br><br>\left(x\right)=\begin{array}{cc}\frac{1-\mathrm{\beta <br><br>}}{1+{\left(\frac{x-x_{\min }}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>1}\right)}^2}& x<<br><br>x_{\min }\\ 1+\frac{\mathrm{\beta <br><br>}*\left(x-x_{\max }\right)}{x_{\max }-x_{\min }}& x_{\min }\le <br><br>x\le <br><br>x_{\max }\\ \frac{1}{1+{\left(\frac{x-x_{\max }}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>2}\right)}^2}& x_{\max }<<br><br>x\end{array}$

where x represents any data values, i.e., x ε

{x₁, . . . , x_k}.

17. The method of claim 13, wherein the polarity of the bias input is negative and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:

$S<br><br>\left(x\right)=\begin{array}{cc}\frac{1}{\underset{\_}{1+{\left(\frac{x-\underset{\_}{x}}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>1}\right)}^2}}& x<<br><br>\underset{\_}{x}\\ 1& x=\underset{\_}{x}\\ \frac{1+\mathrm{\beta <br><br>}}{1+{\left(\frac{x-\underset{\_}{x}}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>2}\right)}^2}& x><br><br>\underset{\_}{x}\end{array}$

where x represents any data values, i.e., x ε

{x₁, . . . , x_k}.

18. The method of claim 13, wherein the polarity of the bias input is positive and wherein the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula:

$S<br><br>\left(x\right)=\begin{array}{cc}\frac{1-\mathrm{\beta <br><br>}}{\underset{\_}{1+{\left(\frac{x-\underset{\_}{x}}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>1}\right)}^2}}& x<<br><br>\underset{\_}{x}\\ 1& x=\underset{\_}{x}\\ \frac{1}{1+{\left(\frac{x-\underset{\_}{x}}{a<br><br>\text{\hspace{1em}<br><br>}<br><br>2}\right)}^2}& x><br><br>\underset{\_}{x}\end{array}$

where x represents any data values, i.e., x ε

{x₁, . . . , x_k}.

19. The method of claim 13, wherein at least one indication is quantitative.

20. The method of claim 13, wherein at least one indication is qualitative.

21. The method of claim 20, wherein each qualitative indication is mapped to a quantitative indication.

22. A method for ranking a plurality of elements, wherein a numerical parameter characterizes each element, the method comprising:

prior to ranking receiving an indication of at least one of fuzziness and bias, and a target range having a minimum numeric value and a maximum numeric value, as inputs to ranking;

ranking a plurality of elements based at least in part on a Lorentzian fuzzy score based at least in part on a numerical parameter characterizing each element, wherein the formula used to calculate each Lorentzian fuzzy score is determined at least in part based on the polarity of the bias input and wherein when the fuzziness indication is set or preset to zero and the polarity of the bias input is positive, the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula;

$S<br><br>\left(x\right)=\begin{array}{cc}0& x<<br><br>x_{\min }\\ 1+\frac{\mathrm{\beta <br><br>}*\left(x-x_{\max }\right)}{x_{\max }-x_{\min }}& x_{\min }\le <br><br>x\le <br><br>x_{\max }\\ 0& x_{\max }<<br><br>x\end{array}$

where x represents any data values, i.e., x ε

{x₁, . . . , x_k},

wherein when the fuzziness indication is set or preset to zero and the polarity of the bias input is negative, the fuzzy score is calculated at least in part by using the following Lorentzian fuzzy score formula;

$S<br><br>\left(x\right)=\begin{array}{cc}0& x<<br><br>x_{\min }\\ 1+\frac{\mathrm{\beta <br><br>}*\left(x-x_{\min }\right)}{x_{\max }-x_{\min }}& x_{\min }\le <br><br>x\le <br><br>x_{\max }\\ 0& x_{\max }<<br><br>x\end{array}$

where x represents any data values, i.e., X ε

{x₁, . . . , x_k}.