SYSTEM AND METHODS FOR ANALYSIS OF DATA
First Claim
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1. A computer method for analyzing data, comprising the steps of:
- encoding a first data set to obtain a first encoded data set;
encoding a second data set to obtain a second encoded data set;
inverting the second encoded data set to obtain an inverted data set;
performing summation of the first encoded data set and the inverted data set to generate a summed data set;
encoding a baseline data set to obtain a baseline encoded data set;
comparing the summed data set to the baseline encoded data set; and
identifying one or more dissimilarities between the first data set and the second data set.
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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.
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Citations
12 Claims
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1. A computer method for analyzing data, comprising the steps of:
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encoding a first data set to obtain a first encoded data set; encoding a second data set to obtain a second encoded data set; inverting the second encoded data set to obtain an inverted data set; performing summation of the first encoded data set and the inverted data set to generate a summed data set; encoding a baseline data set to obtain a baseline encoded data set; comparing the summed data set to the baseline encoded data set; and identifying one or more dissimilarities between the first data set and the second data set. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. Algorithmic components of a computer method for analyzing data, comprising the steps of:
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generating a first sample path from a hidden stochastic source; generating a second sample path from the inverse model of the hidden stochastic source; generating a third sample path from a sum of hidden stochastic sources; estimating a deviation of a symbolic stream from flat white noise.
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Specification