Guo Y., Grossman R. High Performance Data Mining. Scaling Algorithms, Applications and Systems

Special issue on Scaling Data Mining Algorithms, Applications, and Systems to
Massive Data Sets by Applying High Performance Computing Technology
Guest Editors: Yike Guo, Robert GrossmanThis issue contains four papers. They cover important classes of data mining algorithms:
classification, clustering, association rule discovery, and learning Bayesian networks. The
paper by Srivastava et al. presents a detailed analysis of the parallelization strategy of tree
induction algorithms. The paper by Xu et al. presents a parallel clustering algorithm for
distributed memory machines. In their paper, Cheung et al. presents a new scalable algorithm
for association rule discovery and a survey of other strategies. In the last paper of this issue,
Xiang et al. describe an algorithm for parallel learning of Bayesian networks.1. Parallel Formulations of Decision-Tree Classification Algorithms
2. A Fast Parallel Clustering Algorithm for Large Spatial Databases
3. Effect of Data Distribution in Parallel Mining of Associations
4. Parallel Learning of Belief Networks in Large and Difficult Domains