  When considering answering important questions with data, unsupervised data
offers extensive insight opportunity and unique challenges. This study
considers student survey data with a specific goal of clustering students into
like groups with underlying concept of identifying different poverty levels.
Fuzzy logic is considered during the data cleaning and organizing phase helping
to create a logical dependent variable for analysis comparison. Using multiple
data reduction techniques, the survey was reduced and cleaned. Finally,
multiple clustering techniques (k-means, k-modes, and hierarchical clustering)
are applied and compared. Though each method has strengths, the goal was to
identify which was most viable when applied to survey data and specifically
when trying to identify the most impoverished students.
