1 research outputs found
Pattern Dependence Detection using n-TARP Clustering
Consider an experiment involving a potentially small number of subjects. Some
random variables are observed on each subject: a high-dimensional one called
the "observed" random variable, and a one-dimensional one called the "outcome"
random variable. We are interested in the dependencies between the observed
random variable and the outcome random variable. We propose a method to
quantify and validate the dependencies of the outcome random variable on the
various patterns contained in the observed random variable. Different degrees
of relationship are explored (linear, quadratic, cubic, ...). This work is
motivated by the need to analyze educational data, which often involves
high-dimensional data representing a small number of students. Thus our
implementation is designed for a small number of subjects; however, it can be
easily modified to handle a very large dataset. As an illustration, the
proposed method is used to study the influence of certain skills on the course
grade of students in a signal processing class. A valid dependency of the grade
on the different skill patterns is observed in the data