Doctoral Dissertations

Author

Sejun Kim

Abstract

"The problem of clustering is one of the most widely studied area in data mining and machine learning. Adaptive resonance theory (ART), an unsupervised learning clustering algorithm, is a clustering method that can learn arbitrary input patterns in a stable, fast and self-organizing way. This dissertation focuses on unsupervised learning methods, mostly based on variations of ART.

Hierarchical ART clustering is studied by generating a tree of ART units with GPU based parallelization to provide fast and finesse clustering. Experiment results show that the our method achieves significant training speed increase in generating deep ART trees compared with that from non-parallelized version.

In order to handle high dimensional, noisy data more accurately, a hierarchical biclustering ARTMAP (H-BARTMAP) is developed. The nature of biclustering, which considers the correlation of each members in clusters, combined with the concept of hierarchical clustering, provides highly accurate experimental results, especially in bioinformatics data sets.

The third paper focuses on applying the biclustering concept to a supervised learning method, named supervised BARTMAP (S-BARTMAP). Experimental results on high dimensional data sets show that S-BARTMAP is capable of making better predictions compared with those from other math based and machine learning methods

The final paper focuses on solving the semi-supervised support vector machine (S3VM) optimization problem with the aid of value gradient learning (VGL). By applying a reinforcement learning method to a semi-supervised problem results in a solid classification performance in terms of cluster validation, better than algorithms from previous studies"--Abstract, page iv.

Advisor(s)

Wunsch, Donald C.

Committee Member(s)

Sarangapani, Jagannathan, 1965-
Sedigh, Sahra
Corns, Steven
Gosavi, Abhijit

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Computer Engineering

Sponsor(s)

National Science Foundation (U.S.)
NVIDIA Corporation
United States. Army Research Office
Missouri University of Science and Technology. Intelligent Systems Center
Mary K. Finley Missouri Endowment

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2016

Journal article titles appearing in thesis/dissertation

  • A GPU based parallel hierarchical fuzzy ART clustering
  • Hierarchical BARTMAP: A novel innovation in biclustering algorithms
  • Value Gradient Learning based Semi-Supervised Support Vector machines
  • Biclustering based prediction with supervised biclustering ARTMAP

Pagination

xiii, 111 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2016 Sejun Kim, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Pattern recognition systems
Machine learning
Data mining
Fuzzy algorithms

Thesis Number

T 10914

Electronic OCLC #

952595202

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