Efficient Algorithms for Hierarchical Agglomerative Clustering

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  • Author / Creator
    Anandan, Ajay
  • This thesis proposes and evaluates methods to improve two algorithmic ap- proaches for Hierarchical Agglomerative Clustering. These new methods in- crease the scalability and speed of the traditional Hierarchical Agglomerative Clustering algorithm without using any approximations. The first method exploits the characteristics of modern Non-Uniform Memory Access architec- tures, resulting in a parallel algorithm for the stored matrix version of Hier- archical Agglomerative Clustering. The second method uses a data structure called the Cover Tree to speed up the stored data version of the Hierarchical Agglomerative Clustering. For the second method, the thesis proposes both se- quential and parallel algorithms. All methods were experimentally evaluated and compared against the state-of-the-art approaches for high performance clustering. The results demonstrate the superiority of the parallel approaches with respect to all baselines and previous work, and the comparison between the stored matrix and the stored

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Barbosa,Denilson (Department of Computing Science)
  • Examining committee members and their departments
    • Lu, Paul (Department of Computing Science)
    • Sander, Joerg (Department of Computing Science)