Over the years the definition of high performance computing has taken on various forms as a function of the types of technical and creative uses and the underlying semantics of the applications driving them. Traditional definitions often refer to the problem of using high end parallel computers to meet the need of scientific applications. However, high performance computing can also include the need for fast sequential algorithms that target memory and I/O performance. The last decade has seen the growth and importance of grid computing where resources and data are physically distributed. This has led to the development of high performance distributed algorithms over the computational grid, where privacy, security, and resource discovery are all important issues. This year the workshop welcomes papers on all aspects of high performance data mining.

Topics of interest include (but are not limited to):

• Grid-based data mining algorithms and systems
• Distributed techniques for incremental, exploratory and interactive mining
• Distributed techniques for security, privacy preserving data mining
• Peer-to-Peer Data Mining
• High performance data stream mining and management
• Resource and location-aware mining algorithms
• Data mining in mobile environments
• Theoretical foundations for resource-aware mining in a mobile, streaming and/or distributed environment.
• Systems support for resource and location aware data mining
• Efficient, scalable, disk-based, parallel and distributed algorithms for large-scale data mining and pre-processing and post-processing tasks
• Parallel or distributed frameworks for stream management, KDD systems, and parallel or distributed mining
• Applications of parallel and distributed datamining (PDDM) in business, science, engineering, medicine, and other disciplines.