A special issue of Journal of Parallel and Distributed Computing: Models and algorithms for high-performance distributed data mining

摘要

Distributed data mining is well understood as a resource-intensive and time-consuming task which is devoted to extract patterns and regularities from huge amounts of distributed data sets. Classical algorithms, mostly developed in the context of centralized environments, have already been proved to be unsuitable for the goal of mining data in distributed settings. This occurs not only due to conceptual and methodological drawbacks but, most importantly, to novel challenges posed by a distributed, resource-intensive, and time-consuming processing as dictated by high-level specifications of distributed data mining algorithms.