Statistical Optimization of Training Data for Semi-Supervised Text Document Clustering

dc.contributor.advisor Phillips, Joshua Newbold, Cody Renae
dc.contributor.committeemember Pettey, Chrisila
dc.contributor.committeemember Li, Cen
dc.contributor.department Computer Science en_US 2017-10-04T20:15:27Z 2017-10-04T20:15:27Z 2017-06-22
dc.description.abstract Unsupervised machine learning algorithms suffer from uncertainty that results are accurate or useful. In particular, text document clustering algorithms such as Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) give no guarantee that documents are clustered in a manner similar to human readers. Using a semi-supervised approach on text document clustering, we show that the selection of training data can be statistically optimized using LDA and LSA. Using this method, a human reader categorizes a percentage of the data as an analysis step, then feeds the partially-labeled data into bootstrap training and testing steps. Using mutual information to discover which documents were better for training, the algorithm does a post-processing step using the optimized training set. The results show that mutual information values are higher when the statistically optimized training set is used and indicate that human-like performance is better achieved with optimized training data. M.S.
dc.publisher Middle Tennessee State University
dc.subject Afghan war diary
dc.subject Latent dirichlet allocation
dc.subject Latent semantic analysis
dc.subject Topic modeling
dc.subject.umi Computer science
dc.thesis.degreegrantor Middle Tennessee State University
dc.thesis.degreelevel Masters
dc.title Statistical Optimization of Training Data for Semi-Supervised Text Document Clustering
dc.type Thesis
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