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Development of Partially Supervised Kernel-based Proximity Clustering Frameworks and Their Applications Open Access


Other title
Kernel-based clustering
Structural musical segmentation
Time series analysis
Multi-proximity clustering
Partially supervised learning
Active learning
Graph clustering
Fuzzy clustering
Time series clustering
Proximity hints
Type of item
Degree grantor
University of Alberta
Author or creator
Graves, Daniel
Supervisor and department
Pedrycz, Witold (Electrical and Computer Engineering)
Examining committee member and department
Musilek, Petr (Electrical and Computer Engineering)
Robinson, Aminah (Civil Engineering)
Reformat, Marek (Electrical and Computer Engineering)
Cockburn, Bruce (Electrical and Computer Engineering)
Leung, Henry (External Reader, Electrical and Computer Engineering, University of Calgary)
Department of Electrical and Computer Engineering

Date accepted
Graduation date
Doctor of Philosophy
Degree level
The focus of this study is the development and evaluation of a new partially supervised learning framework. This framework belongs to an emerging field in machine learning that augments unsupervised learning processes with some elements of supervision. It is based on proximity fuzzy clustering, where an active learning process is designed to query for the domain knowledge required in the supervision. Furthermore, the framework is extended to the parametric optimization of the kernel function in the proximity fuzzy clustering algorithm, where the goal is to achieve interesting non-spherical cluster structures through a non-linear mapping. It is demonstrated that the performance of kernel-based clustering is sensitive to the selection of these kernel parameters. Proximity hints procured from domain knowledge are exploited in the partially supervised framework. The theoretic developments with proximity fuzzy clustering are evaluated in several interesting and practical applications. One such problem is the clustering of a set of graphs based on their structural and semantic similarity. The segmentation of music is a second problem for proximity fuzzy clustering, where the aim is to determine the points in time, i.e. boundaries, of significant structural changes in the music. Finally, a time series prediction problem using a fuzzy rule-based system is established and evaluated. The antecedents of the rules are constructed by clustering the time series using proximity information in order to localize the behavior of the rule consequents in the architecture. Evaluation of these efforts on both synthetic and real-world data demonstrate that proximity fuzzy clustering is well suited for a variety of problems.
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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