Geometrization for pattern recognition, data analysis, data merging, and multiple criteria decision making
First Claim
1. A data scaling method comprising the steps of:
- (a) receiving data;
(b) forming two partitions of the received data;
(c) applying admissible geometrization to the partitioned received data to produce admissibly transformed data; and
(d) interpreting the admissibly transformed data as scaled data.
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Accused Products
Abstract
An analyzer/classifier/synthesizer/prioritizing tool for data comprises use of an admissible geometrization process with data transformed and partitioned by an input process into one or more input matrices and one or more partition classes and one or more scale groups. The data to be analyzed/classified/synthesized/prioritized is processed by an admissible geometrization technique such as 2-partition modified individual differences multidimensional scaling (2p-IDMDS) to produce at least a measure of geometric fit. Using the measure of geometric fit and possibly other 2p-IDMDS output, a back end process analyzes, synthesizes, classifies, and prioritizes data through patterns, structure, and relations within the data.
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Citations
53 Claims
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1. A data scaling method comprising the steps of:
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(a) receiving data; (b) forming two partitions of the received data; (c) applying admissible geometrization to the partitioned received data to produce admissibly transformed data; and (d) interpreting the admissibly transformed data as scaled data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A data scaling method comprising the steps of:
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(a) receiving data; (b) partitioning the received data; (c) forming one or more symmetric matrices from the partitioned received data; (d) forming a second partition of the received data; (e) associating a scale type to each subset of the second partition of the received data; (f) applying admissible geometrization to the partitioned received data to produce admissibly transformed data; and (g) interpreting the admissibly transformed data as scaled data. - View Dependent Claims (20, 21, 22, 23, 24)
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25. A data scaling method comprising the steps of:
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(a) receiving data; (b) creating a plurality of rearrangements of the received data; (c) applying admissible geometrization to each of the plurality of rearrangements of the received data to produce a plurality of sets of admissibly transformed data; (d) merging the plurality of sets of admissibly transformed data; and (e) interpreting the merged data as scaled data.
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26. A method for admissibly merging data comprising the steps of:
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(a) receiving data; (b) forming two partitions of the received data; (c) applying admissible geometrization to the partitioned received data to produce admissibly transformed data; and (d) interpreting the admissibly transformed data as scaled data in processing the admissibly transformed data to provide merged data. - View Dependent Claims (27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42)
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43. A data merging method comprising the steps of:
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(a) receiving data, (b) creating a plurality of rearrangements of the received data; (c) applying admissible geometrization to each of the plurality of rearrangements of the received data to produce a plurality of sets of admissibly transformed data; (d) merging the plurality of sets of admissibly transformed data; and (e) interpreting the admissibly transformed data as scaled data in processing the merged admissibly transformed data to provide merged data.
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44. A data scaling method comprising the steps of:
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(a) receiving intermixed scale type data; (b) forming two partitions of the received intermixed scale type data; (c) applying admissible geometrization to the partitioned received intermixed scale type data to produce admissibly transformed data; and (d) interpreting the admissibly transformed data as scaled data.
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45. A data scaling method comprising the steps of:
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(a) receiving intermixed scale type data; (b) partitioning the received intermixed scale type data; (c) forming one or more symmetric matrices from the partitioned received data; (d) forming a second partition of the received data; (e) associating a scale type to each subset of the second partition of the received data; (f) applying admissible geometrization to the partitioned received data to produce admissibly transformed data; and (g) interpreting the admissibly transformed data as scaled data. - View Dependent Claims (46, 47)
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48. A method for admissibly merging data comprising the steps of:
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(a) receiving intermixed scale type data; (b) forming two partitions of the received intermixed scale type data; (c) applying admissible geometrization to the partitioned received intermixed scale type data to produce admissibly transformed data; and (d) interpreting the admissibly transformed data as scaled data in processing the admissibly transformed data to provide merged data.
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49. A method for admissibly merging data comprising the steps of:
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(a) receiving intermixed scale type data; (b) partitioning the received intermixed scale type data; (c) forming one or more symmetric matrices from the partitioned received data; (d) forming a second partition of the received data; (e) associating a scale type to each subset of the second partition of the received data; (f) applying admissible geometrization to the partitioned received data to produce admissibly transformed data; and (g) interpreting the admissibly transformed data as scaled data in processing the admissibly transformed data to provide merged data. - View Dependent Claims (50, 51)
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52. A method for admissibly prioritizing preference data comprising the steps of:
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(a) receiving preference data; (b) forming two partitions of the received preference data; (c) applying admissible geometrization to the received preference data to produce admissibly transformed data; and (d) interpreting the admissibly transformed data as scaled data in processing the admissibly transformed data to provide priorities. - View Dependent Claims (53)
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Specification