in conjunction with 15th International Parallel and Distributed Processing Symposium

As the volume of data increases, it is clear that both parallel and distributed data mining techniques are required to make the whole knowledge discovery process scalable and interactive. This workshop called for papers on high performance parallel and distributed methods, as well as mining on distributed and heterogeneous datasets. Topics of interest included: • Efficient, scalable, disk-based, parallel and distributed algorithms for large-scale data mining tasks • New algorithms for common data mining methods such as association rules, sequences, clas-sification, clustering, deviation detection, etc. • Pre-processing and post-processing operations like sampling, feature selection, data reduction and transformation, rule grouping and pruning, etc. • Incremental, exploratory and interactive mining • Meta-mining, coping with distributed and/or heterogeneous datasets • Integration of mining with parallel/distributed databases and data-warehouses • Mining non-traditional datasets, such as large scientific databases • Frameworks for KDD systems, and parallel or distributed mining • Agent based approaches for PDD