Publication Date
2006
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
Abstract
The Web contains massive amount of documents from across the globe to the point where it has become impossible to classify them manually. This project’s goal is to find a new method for clustering documents that are as close to humans’ classification as possible and at the same time to reduce the size of the documents. This project uses a combination of Latent Semantic Indexing (LSI) with Singular Value Decomposition (SVD) calculation as well as Support Vector Machine (SVM) classification. With SVD, data sets are decomposed and can be truncated to reduce the data sets size. The reduced data set will then be used to cluster. With SVM, clustered data set is used for training to allow new data to be classified based on SVM’s prediction. The project’s result show that the method of combining SVD and SVM is able to reduce data size and classifies documents reasonably compared to humans’ classification.
Recommended Citation
Ngo, Tam P., "Clustering High Dimensional Data Using SVM" (2006). Master's Projects. 33.
DOI: https://doi.org/10.31979/etd.ns2s-ejvc
https://scholarworks.sjsu.edu/etd_projects/33
