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Permanent link (DOI): https://doi.org/10.7939/R34M91K17

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A Parameter Selection Framework For Semi-Supervised Clustering Algorithms Open Access

Descriptions

Other title
Subject/Keyword
Clustering parameter
Parameter selection
Semi-supervised clustering
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Pourrajabi, Mojgan
Supervisor and department
Sander, Joerg (Computing Science)
Examining committee member and department
Campello, Ricardo (Computing Science, University of Säo Paulo at Säo Carlos, Brazil)
Goebel, Randolph (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2013-09-26T11:25:36Z
Graduation date
2013-11
Degree
Master of Science
Degree level
Master's
Abstract
Many clustering techniques require parameter settings and depending on an algorithms sensitivity to the parameter, the choice of the parameter value can be very important. Several approaches have been proposed to find the “best” value of the clustering parameter for the different unsupervised clustering methods. We introduce a general method, denoted as “Cross-validation framework for finding clustering parameters” (CVCP). Given a data set, CVCP selects the “best” parameter value for a semi-supervised clustering method based on available constraints or labels that are given as input to a semi-supervised clustering method. CVCP is evaluated based on selecting the “best” value of k for a semi-supervised Kmeans-based clustering algorithm and the “best” value of MinPts for a semi-supervised density-based clustering algorithm. Our experimental results show that using the framework to select parameters can significantly improve the expected performance of a semi-supervised clustering method when appropriate parameter values often have to be “guessed”.
Language
English
DOI
doi:10.7939/R34M91K17
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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