Economic Control of Quality of Manufactured Product.

An efficient recommendation generation using relevant Jaccard similarity

Decision support system for safety improvement: An approach using multiple correspondence analysis, t-SNE algorithm and K-means clustering

Development of new seed with modified validity measures for k-means clustering

Freestyle text data such as surveys, complaint transcripts, customer ratings, or maintenance squawks can provide critical information for quality engineering. Exploratory text data analysis (ETDA) ...

Exploratory text data analysis for quality hypothesis generation

Many decision problems are set in changing environments. For example, determining the optimal investment in cyber maintenance depends on whether there is evidence of an unusual vulnerability, such as “Heartbleed,” that is causing an especially high rate of incidents. This gives rise to the need for timely information to update decision models so that optimal policies can be generated for each decision period. Social media provide a streaming source of relevant information, but that information needs to be efficiently transformed into numbers to enable the needed updates. This article explores the use of social media as an observation source for timely decision making. To efficiently generate the observations for Bayesian updates, we propose a novel computational method to fit an existing clustering model. The proposed method is called k-means latent Dirichlet allocation (KLDA). We illustrate the method using a cybersecurity problem. Many organizations ignore “medium” vulnerabilities identified during peri...

https://par.nsf.gov/servlets/purl/10058430

Timely Decision Analysis Enabled by Efficient Social Media Modeling

....................................................................................................................... ii ACKNOWLEDGMENTS .................................................................................................. v VITA ................................................................................................................................ viii FIELDS OF STUDY.......................................................................................................... ix PUBLICATIONS ............................................................................................................... ix TABLE OF CONTENTS .................................................................................................... x LIST OF TABLES ............................................................................................................ xv LIST OF FIGURES ......................................................................................................... xvi CHAPTER

Hierarchical Text Topic Modeling with Applications in Social Media-Enabled Cyber Maintenance Decision Analysis and Quality Hypothesis Generation

: 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.

/pdf/k-means-subject-matter-expert-refined-topic-model-4o8bm7yhb9.pdf

K-Means Subject Matter Expert Refined Topic Model Methodology

Due to the rapid growth of large size text data from Internet sources like Twitter, social media platforms have become the more popular sources to be utilized to extract information. The extracted text information is then further converted to number through a series of data transformation and then analyzed through text analytics models for decision-making problems. Among the text analytics models, one particular common and popular one is based on Latent Dirichlet Allocation (LDA), which is a topic model method with the topics being clusters of words in the documents associated with fitted multivariate statistical distributions. However, these models are often poor estimators of topic proportions. Hence, this paper proposes a timely topic score technique for social media text data visualization, which is based on a point system from topic models to support text signaling. This importance score system is intended to mitigate the weakness of topic models by employing the topic proportion outputs and assigning importance points to present text topic trends. The technique then generates visualization tools to show topic trends over the studied time period and then further facilitate decision-making problems. Finally, this paper studies two real-life case examples from Twitter text sources and illustrates the efficiency of the methodology.

/pdf/social-media-text-data-visualization-modeling-a-timely-topic-31jozm0jf8.pdf

Zhenhuan Sui

Papers

Timely Decision Analysis Enabled by Efficient Social Media Modeling

Exploratory text data analysis for quality hypothesis generation

Hierarchical Text Topic Modeling with Applications in Social Media-Enabled Cyber Maintenance Decision Analysis and Quality Hypothesis Generation

K-Means Subject Matter Expert Refined Topic Model Methodology

Social Media Text Data Visualization Modeling: A Timely Topic Score Technique