Graduation Year
2017
Date of Submission
4-2017
Document Type
Open Access Senior Thesis
Degree Name
Bachelor of Arts
Department
Mathematics
Reader 1
Deanna Needell
Reader 2
Blake Hunter
Terms of Use & License Information
Rights Information
© 2017 Matthew L Aven
Abstract
This paper explores the potential applications of existing spectral clustering algorithms to real life problems through experiments on existing road traffic data. The analysis begins with an overview of previous unsupervised machine learning techniques and constructs an effective spectral clustering algorithm that demonstrates the analytical power of the method. The paper focuses on the spectral embedding method’s ability to project non-linearly separable, high dimensional data into a more manageable space that allows for accurate clustering. The key step in this method involves solving a normalized eigenvector problem in order to construct an optimal representation of the original data.
While this step greatly enhances our ability to analyze the relationships between data points and identify the natural clusters within the original dataset, it is difficult to comprehend the eigenvalue representation of the data in terms of the original input variables. The later sections of this paper will explore how the careful framing of questions with respect to available data can help researchers extract tangible decision driving results from real world data through spectral clustering analysis.
Recommended Citation
Aven, Matthew, "Daily Traffic Flow Pattern Recognition by Spectral Clustering" (2017). CMC Senior Theses. 1597.
https://scholarship.claremont.edu/cmc_theses/1597
