Mixture Models for Exploratory Data Analysis

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The Naval Research Laboratory (NRL) has developed a method to automatically discover and evaluate candidate mathematical models within unstructured data and classify each data point with a probability of fitting each discovered model. A model may be any mathematical shape, including closed curves and geometric shapes, general functions such as Gaussians, polynomials, exponentials, or transcendentals, or any combination of the above. Once a set of models is determined to fit the data set, a probability may be assigned to each data point to measure the likelihood that a given point “belongs to” a particular function, i.e. that it is consistent with that model. A classification of these points may be derived from these probabilities. This analytical tool is intended for data mining and visual analysis of large, complex data sets in the intelligence community. It has been used with GIS data, weather data, crime data, and other multivariate data sets as well. This software is coded in C++ and is available for commercialization.NRL Technology Transfer Officehttp://www.nrl.navy.mil/techtransfer/fs.php?fs_id=IT07Naval Research Laboratory websitehttp://www.nrl.navy.mil

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MIXTURE MODELS FOR EXPLORATORY DATA ANALYSIS
Advantages/Features Model abstraction seamlessly considers different types of functions as equally possible candidates Does not require an estimate of the number of underlying mathematical models Allows data points to be probabilistically classified into multiple models or identified as outliers Includes a few parameters that the analyst can use to adjust the output Applications Intelligence analysis Research & education Economics, finance, real estate, and business analysis Targeted, demographics-based marketing For more information contact: Rita Manak, Ph.D. Head, Technology Transfer Office 202 767-3083 [email protected] Identification Number: IT07 Available for License: US Patent Pub. No. 2010-0042563.

The Naval Research Laboratory (NRL) has developed a method to automatically discover and evaluate candidate mathematical models within unstructured data and classify each data point with a probability of fitting each discovered model. A model may be any mathematical shape, including closed curves and geometric shapes, general functions such as Gaussians, polynomials, exponentials, or transcendentals, or any combination of the above. Once a set of models is determined to fit the data set, a probability may be assigned to each data point to measure the likelihood that a given point  belongs to  a particular function, i.e. that it is consistent with that model. A classification of these points may be derived from these probabilities. This analytical tool is intended for data mining and visual analysis of large, complex data sets in the intelligence community. It has been used with GIS data, weather data, crime data, and other multivariate data sets as well. This software is coded in C++ and is available for commercialization.

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