Singular Value Decomposition for Dimensionality Reduction in Unsupervised Text Learning Problems

摘要：

Partitioning vast amounts of text documents is a challenging problem due to a high dimensional representation of the documents. In this study, we investigate the quality of text document clustering when Singular Value Decomposition (SVD) is used to reduce the dimension of the documents. The results show that the quality of the clusters is very comparable to that of when the dimensions are not reduced. In addition, the computational cost to cluster documents can be reduced significantly when the clustering is done on a small dimension.

关键词： Singular Value Decomposition Unsupervised Text Learning

作者: Taufik Fuadi Abidin Bustami Yusuf Munzir Umran

作者单位: Data Mining and Information Retrieval Research Group Mathematics Department College of Mathematics and Natural Science Syiah Kuala University - Banda Aceh - Indonesia

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Education Technology and Computer(第二届IEEE教育技术与计算机国际会议 ICETC 2010)

会议地点: 上海

会议语种:英文

页码: 422-426

在线出版日期: 2010-06-22（万方平台首次上网日期，不代表论文的发表时间）