Contains four refereed papers covering important classes of data mining algorithms: classification, clustering, association rule discovery, and learning Bayesian networks. Srivastava et al present a detailed analysis of the parallelization strategy of tree induction algorithms. Xu et al present a parallel clustering algorithm for distributed memory machines. A new scalable algorithm for association rule discovery and a survey of other strategies is covered by Cheung et al. The final paper, written by Xiang et al, describes an algorithm for parallel learning of Bayesian networks. The papers aim to take a practical approach to large scale mining applications and increase useable knowledge concerning high performance computing technology. Lacks a subject index.
