External Support Vector Machine Clustering

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year

2006

Author dc:creator

• McChesney, Charlie

Contributors dc:contributor

• Winters-Hilt, Stephen
• Fu, Bin
• Tu, Shengru

Repository record dc:identifier

https://scholarworks.uno.edu/td/409

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-1430