Center for Industrial Mathematics and Statistics » REU 2013 – Analyzing Databases for Patterns in Customer Usage for GenomeQuest

Overview

Industrial Sponsor: Richard Resnick, CEO of GenomeQuest

Faculty Advisor: Matthew Willyard

Our project focuses on developing meaningful patterns from GenomeQuest's customer usage data files through the use of the Knowledge Discovery in Databases (KDD) process. We are looking for patterns within their customer usage keeping track of patterns among regions and types of clientele along with any other grouping. This involves extensive processing and transformation of the data to apply various data mining techniques to distinguish patterns. We are using multiple data mining techniques including logistic regression, decision trees, decision forests, clustering, association rules and neural networks. The procedure and application to our project of each technique is examined along with preliminary results.

Researchers

Anastasia Bergara, Alyssa Cuyjet, Matt Smith, and Brittany Street