You are here

Foundations of Perturbation Robust Clustering

Title: Foundations of Perturbation Robust Clustering.
Name(s): Moore, Jarrod, author
Ackerman, Margareta, professor co-directing thesis
Tyson, Gary Scott, professor co-directing thesis
Haiduc, Sonia, committee member
Zhao, Peixiang, committee member
Florida State University, degree granting institution
College of Arts and Sciences, degree granting college
Department of Scientific Computing, degree granting department
Type of Resource: text
Genre: Text
Master Thesis
Issuance: monographic
Date Issued: 2017
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource (31 pages)
Language(s): English
Abstract/Description: Clustering is a fundamental data mining tool that aims to divide data into groups of similar items. Intuition about clustering reflects the ideal case -- exact data sets endowed with flawless dissimilarity between individual instances. In practice however, these cases are in the minority, and clustering applications are typically characterized by noisy data sets with approximate pairwise dissimilarities. As such, the efficacy of clustering methods necessitates robustness to perturbations. In this paper, we address foundational questions on perturbation robustness, studying to what extent can clustering techniques exhibit this desirable characteristic. Our results also demonstrate the type of cluster structures required for robustness of popular clustering paradigms.
Identifier: FSU_SUMMER2017_Moore_fsu_0071N_13913 (IID)
Submitted Note: A Thesis submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Science.
Degree Awarded: Summer Semester 2017.
Date of Defense: May 4, 2017.
Bibliography Note: Includes bibliographical references.
Advisory Committee: Margareta Ackerman, Professor Co-Directing Thesis; Gary Tyson, Professor Co-Directing Thesis; Sonia Haiduc, Committee Member; Peixiang Zhao, Committee Member.
Subject(s): Artificial intelligence
Persistent Link to This Record:
Owner Institution: FSU

Choose the citation style.
Moore, J. (2017). Foundations of Perturbation Robust Clustering. Retrieved from