An Efficient Spectral Method for Document Cluster Ensemble
- Resource Type
- Conference
- Authors
- Xu, Sen; Lu, Zhimao; Gu, Guochang
- Source
- 2008 The 9th International Conference for Young Computer Scientists Young Computer Scientists, 2008. ICYCS 2008. The 9th International Conference for. :808-813 Nov, 2008
- Subject
- Computing and Processing
Clustering algorithms
Partitioning algorithms
Laplace equations
Computational efficiency
Educational institutions
Eigenvalues and eigenfunctions
Pattern analysis
Machine learning algorithms
Bagging
Boosting
clustering analysis
cluster ensemble
spectral clustering
document clustering
- Language
Cluster ensemble techniques have been recently shown to be effective in improving the accuracy and stability of single clustering algorithms. A critical problem in cluster ensemble is how to combine multiple clusterers to yield a final superior clustering result. In this paper, we present an efficient spectral graph theory-based ensemble clustering method feasible for large scale applications such as document clustering. Since the EigenValue Decomposition (EVD) of Laplacian is formidable for large document sets, we first transform it to a Singular Value Decomposition (SVD) problem, and then an equivalent EVD is performed. Experiments show that our spectral algorithm yields better clustering results than other cluster ensemble techniques without high computational cost.