Accession Number : AD1028777


Title :   K-Means Subject Matter Expert Refined Topic Model Methodology


Descriptive Note : Technical Report,01 Oct 2015,30 Dec 2016


Corporate Author : TRADOC Analysis Center - Monterey Monterey United States


Personal Author(s) : Parker,Nathan ; Allen,Theodore T ; Sui,Zhenhuan


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1028777.pdf


Report Date : 01 Jan 2017


Pagination or Media Count : 45


Abstract : We propose an innovative technique using K-means clustering to estimate the posterior topic distributions in Latent Dirichlet topic models as an alternative to the collapsed Gibbs sampling technique. This research also develops a topic modeling software instantiation of the K-means Subject Matter Expert Refined Topic methodology using the Visual Basic for Applications programming language. This topic modeling software is deployable across the majority of the Department of Defense computing environments and allows analysts to develop topic models using a graphical user interface.


Descriptors :   Software , computer programming , programming languages , Methodology , models , clustering , software development


Subject Categories : Computer Programming and Software


Distribution Statement : APPROVED FOR PUBLIC RELEASE