Advances in Distributed and Parallel Knowledge Discovery
Knowledge discovery and data mining (KDD) deals with the problem of extracting interesting associations, classifiers, clusters, and other patterns from data. The emergence of network-based distributed computing environments has introduced an important new dimension to this problem—distributed sources of data. Traditional centralized KDD typically requires central aggregation of distributed data, which may not always be feasible because of limited network bandwidth, security concerns, scalability problems, and other practical issues. Distributed knowledge discovery (DKD) works with the merger of communication and computation by analyzing data in a distributed fashion. This technology is particularly useful for large heterogeneous distributed environments such as the Internet, intranets, mobile computing environments, and sensor-networks.
When the datasets are large, scaling up the speed of the KDD process is crucial. Parallel knowledge discovery (PKD) techniques address this problem by using high performance multi-processor machines. This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques.