Date of Award

8-9-2006

Degree Type

Thesis

Degree Name

M.S.

Degree Program

Computer Science

Department

Computer Science

Major Professor

Winters-Hilt, Stephen

Second Advisor

Fu, Bin

Third Advisor

Tu, Shengru

Fourth Advisor

Abdelguerfi, Mahdi

Abstract

The external-Support Vector Machine (SVM) clustering algorithm clusters data vectors with no a priori knowledge of each vector's class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data points that are mislabeled and a large distance from the SVM hyperplane. The SVM is then iteratively rerun followed by more label swapping until no more progress can be made. After this process, a high percentage of the previously unknown class labels of the data set will be known. With sub-cluster identification upon iterating the overall algorithm on the positive and negative clusters identified (until the clusters are no longer separable into sub-clusters), this method provides a way to cluster data sets without prior knowledge of the data's clustering characteristics, or the number of clusters.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

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