Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references. Available also from UMI/Dissertation Abstracts International.

Not available / text

Thesis (M.S.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed December 4, 2007) Includes bibliographical references (p. 67-69).

Self-Organizing Maps (SOM) can provide a visualization for multi-dimensional data with two dimensional mappings. By applying unsupervised learning techniques to SOM representations, we can further enhance visual inspection for change detection. In order to obtain a more accurate measurement for the changes of self-organizing maps beyond simple visual inspection, we introduce the Gaussian Mixture Model (GMM) and Kullback-Leibler Divergence (KLD) on top of SOM trained maps. The main contribution in this dissertation focuses on adding numerical methods to SOM algorithms, with anomaly detection as example domain. Through extensive traced-based simulations, it is observed that our techniques can uncover anomalies with an accuracy of 100% at an anomaly mixture-rate as low as 12% from the CTU-13 dataset. Tuning of the KLD threshold further reduces the mixture-rate to 7%, significantly augmenting visual inspection to assist in detecting low-rate anomalies. Suitable hierarchical and distributed SOM-based approaches are also explored, along with other approaches in the literature. Hierarchies in SOM can show the correlations among the neural cells on the self-organizing maps. In order to obtain a higher accuracy for anomaly detection, a new dimension of labels is suggested to be added in the second layer of SOM training. Also for more general distributed SOM-based algorithms, we investigate the use of principal component analysis (PCA) for the separation of dimensions. With the transformed dataset from PCA, the inner dependencies can be reserved in a manageable scale. As a case study, this dissertation uses a SOM-based approach for anomaly detection in Supervisory Control And Data Acquisition (SCADA) networks. We further investigate the use of SOM for the Quality of Service (QoS) in the scenario of wireless SCADA networks. Solving the problem of long computing time of optimizing the cached contents, the new SOM-based approach can also learn and predict the sub-optimal locations for the caching while maintaining a prediction error of 28%. / Graduate

Thesis (M.S.) -- Central Connecticut State University, 2009. / Thesis advisor: Daniel Larose. "... in partial fulfillment of the requirements for the degree of Master of Science in Data Mining." Includes bibliographical references (leaves 37-39). Also available via the World Wide Web.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 119-122).

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

Thesis (Ph.D.) -- University of Rhode Island, 2007 / Typescript. Includes bibliographical references (leaves 73-82).

Thesis (M.S)--Ohio University, March, 2005. / Includes bibliographical references (p. 78-82)

Thesis (M.S)--Ohio University, March, 2005. / Title from PDF t.p. Includes bibliographical references (p. 78-82)