This paper proposes a novel hierarchical learning strategy to deal with the data sparseness problem in relation extraction by modeling the commonality among related classes. For e...
Temporal databases provide built-in supports for efficient recording and querying of time-evolving data. In this paper, data clustering issues in temporal database environment are...
Many existing spectral clustering algorithms share a conventional graph partitioning criterion: normalized cuts (NC). However, one problem with NC is that it poorly captures the g...
In the context of genome research, the method of gene expression analysis has been used for several years. Related microarray experiments are conducted all over the world, and con...
In this work we investigate the feasibility and effectiveness of unsupervised tissue clustering and classification algorithms for DTI data. Tissue clustering and classification ...