Clusters of workstations are becoming popular platforms for parallel computing, but performance on these systems is more complex and harder to predict than on traditional parallel...
Geetanjali Sampemane, Scott Pakin, Andrew A. Chien
The problem of assessing the reliability of clusters patients identified by clustering algorithms is crucial to estimate the significance of subclasses of diseases detectable at b...
Malware clustering and classification are important tools that enable analysts to prioritize their malware analysis efforts. The recent emergence of fully automated methods for ma...
The discovery and construction of inherent regions in large spatial datasets is an important task for many research domains such as climate zoning, eco-region analysis, public heal...
Nearest neighborhood consistency is an important concept in statistical pattern recognition, which underlies the well-known k-nearest neighbor method. In this paper, we combine th...