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Daily Traffic Flow Pattern Recognition By Spectral Clustering, Matthew Aven
Daily Traffic Flow Pattern Recognition By Spectral Clustering, Matthew Aven
CMC Senior Theses
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 …