K-means clustering is one of the most popular cluster analysis techniques, and Windows Azure is Microsoft's cloud computing platform. With the rapidly growing volume of data, cloud computing is a possible solution for analyzing large data sets. The goal of this project is to implement parallel k-means clustering on Windows Azure and evaluate the performance by utilizing different numbers of virtual machines (1–8) in order to calculate new centroids.

Committee
Dr. Yi Pan (chair)
Dr. Raj Sunderraman