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 :

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