Clustering Via Supervised Support Vector Machines

An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labeled. Once this initialization step is complete, the SVM confidence parameters for classification on each of the training instances can be accessed. The lowest confidence data (e.g., the worst of the mislabeled data) then has its labels switched to the other class label. The SVM is then re-run on the data set (with partly re-labeled data). The repetition of the above process improves the separability until there is no misclassification. Variations on this type of clustering approach are shown.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year

2008

Author dc:creator

• Merat, Sepehr

Contributors dc:contributor

• Winters-Hilt, Stephen
• Summa, Christopher
• Zhu, Dongxiao

• clustering
• machine learning
• pattern recognition
• support vector machines
• supervised learning
• unsupervised learning

Repository record dc:identifier

https://scholarworks.uno.edu/td/857

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-1837