Large-scale Clustering using Random Sketching and Validation

Thumbnail Image

Persistent link to this item

View Statistics

Journal Title

Journal ISSN

Volume Title


Large-scale Clustering using Random Sketching and Validation

Published Date




Thesis or Dissertation


The advent of high-speed Internet, modern devices and global connectivity has introduced the world to massive amounts of data, that are being generated, communicated and processed daily. Extracting meaningful information from this humongous volume of data is becoming increasingly challenging even for high-performance and cloud computing platforms. While critically important in a gamut of applications, clustering is computationally expensive when tasked with high-volume high-dimensional data. To render such a critical task affordable for data-intensive settings, this thesis introduces a clustering framework, named random sketching and validation (SkeVa). This framework builds upon and markedly broadens the scope of random sample and consensus RANSAC ideas that have been used successfully for robust regression. Four main algorithms are introduced, which enable clustering of high-dimensional data, as well as subspace clustering for data generated by unions of subspaces and clustering of large-scale networks. Extensive numerical tests compare the SkeVa algorithms to their state-of-the-art counterparts and showcase the potential of the SkeVa frameworks.


University of Minnesota M.S.E.E. thesis. August 2015. Major: Electrical Engineering. Advisor: Georgios Giannakis. 1 computer file (PDF); viii, 102 pages.

Related to



Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Traganitis, Panagiotis. (2015). Large-scale Clustering using Random Sketching and Validation. Retrieved from the University Digital Conservancy,

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.