  The abundance of text data being produced in the modern age makes it
increasingly important to intuitively group, categorize, or classify text data
by theme for efficient retrieval and search. Yet, the high dimensionality and
imprecision of text data, or more generally language as a whole, prove to be
challenging when attempting to perform unsupervised document clustering. In
this thesis, we present two novel methods for improving unsupervised document
clustering/classification by theme. The first is to improve document
representations. We look to exploit "term neighborhoods" and "blur" semantic
weight across neighboring terms. These neighborhoods are located in the
semantic space afforded by "word embeddings." The second method is for cluster
revision, based on what we deem as "stochastic barcoding", or "S- Barcode"
patterns. Text data is inherently high dimensional, yet clustering typically
takes place in a low dimensional representation space. Our method utilizes
lower dimension clustering results as initial cluster configurations, and
iteratively revises the configuration in the high dimensional space. We show
with experimental results how both of the two methods improve the quality of
document clustering. While this thesis elaborates on the two new conceptual
contributions, a joint thesis by David Yan details the feature transformation
and software architecture we developed for unsupervised document
classification.
