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Distributed Hierarchical Clustering

Loganathan, Satish Kumar
http://orcid.org/0000-0002-0599-5161
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1544001912266574

Abstract Details

2018, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Hierarchical clustering is an inherently sequential algorithm designed for datasets that can fit in the memory of a single stand-alone system. In this thesis, we extend the agglomerative hierarchical clustering algorithm to distributed data environments where both the storage and computational resources are decentralized. We specifically target environments where the data is horizontally partitioned.
Raj Bhatnagar, Ph.D. (Committee Chair)
Gowtham Atluri, Ph.D. (Committee Member)
Ali Minai, Ph.D. (Committee Member)
82 p.
Computer Science
Distributed Data Mining; Distributed Clustering; Hierarchical Clustering

Recommended Citations

Citations

  • Loganathan, S. K. (2018). Distributed Hierarchical Clustering [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1544001912266574

    APA Style (7th edition)

  • Loganathan, Satish Kumar. Distributed Hierarchical Clustering. 2018. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1544001912266574.

    MLA Style (8th edition)

  • Loganathan, Satish Kumar. "Distributed Hierarchical Clustering." Master's thesis, University of Cincinnati, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1544001912266574

    Chicago Manual of Style (17th edition)

ucin1544001912266574
156
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This open access ETD is published by University of Cincinnati and OhioLINK.