Workshop proceedings have been published in LNCS Vol. These include a radiology storage infrastructure for the National Library of Medicine Next Generation Internet program , a digital government database for Census and demographic data in the City of Philadelphia, and a parallel networking project using high speed optical networks to enable distributed parallel data computing. Data mining and knowledge discovery on large amounts of data can benefit of the use of parallel computers both to improve performance and quality of data selection. When data mining tools are implemented on high-performance parallel computers, they can analyze massive databases in a reasonable time. Faster processing also means that users can experiment with more models to understand complex data.
High performance makes it practical for users to analyze greater quantities of data.
This talk analyzes and presents different forms of parallelism that can be exploited in data mining techniques and algorithms. The main goal of the talk is to discuss data mining techniques on parallel architectures and show how large scale data mining and knowledge discovery applications can be scalable by using systems, tools and performance offered by parallel processing systems. Finally, the talk outlines current research issues in high-performance data mining and discusses perspectives in this area. In the spirit of the workshop, submission of works in progress are encouraged as well.