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Effective and Efficient Approach for Clusterability Evaluation

Title: An Effective and Efficient Approach for Clusterability Evaluation.

Inaccessible until May 8, 2020 due to copyright restrictions.

Name(s): Adolfsson, Andreas, author
Ackerman, Margareta, professor co-directing thesis
Brownstein, Naomi Chana, professor co-directing thesis
Haiduc, Sonia, committee member
Tyson, Gary Scott, committee member
Florida State University, degree granting institution
College of Arts and Sciences, degree granting college
Department of Computer Science, degree granting department
Type of Resource: text
Genre: Text
Master Thesis
Issuance: monographic
Date Issued: 2016
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource (45 pages)
Language(s): English
Abstract/Description: Clustering is an essential data mining tool that aims to discover inherent cluster structure in data. As such, the study of clusterability, which evaluates whether data possesses such structure, is an integral part of cluster analysis. Yet, despite their central role in the theory and application of clustering, current notions of clusterability fall short in two crucial aspects that render them impractical; most are computationally infeasible and others fail to classify the structure of real datasets. In this thesis, we propose a novel approach to clusterability evaluation that is both computationally efficient and successfully captures the structure in real data. Our method applies multimodality tests to the (one-dimensional) set of pairwise distances based on the original, potentially high-dimensional data. We present extensive analyses of our approach for both the Dip and Silverman multimodality tests on real data as well as 17,000 simulations, demonstrating the success of our approach as the first practical notion of clusterability.
Identifier: FSU_SUMMER2017_Adolfsson_fsu_0071N_13478 (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 2016.
Date of Defense: May 25, 2016.
Keywords: Clusterability, Clustering, Multimodality, Real data, Simulations
Bibliography Note: Includes bibliographical references.
Advisory Committee: Margareta Ackerman, Professor Co-Directing Thesis; Naomi Brownstein, Professor Co-Directing Thesis; Sonia Haiduc, Committee Member; Gary Tyson, Committee Member.
Subject(s): Computer science
Persistent Link to This Record:
Owner Institution: FSU

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Adolfsson, A. (2016). An Effective and Efficient Approach for Clusterability Evaluation. Retrieved from